fix nan

app_inference.py

@@ -1,6 +1,5 @@
 from PytorchBoot.application import PytorchBootApplication
 from runners.inferencer import Inferencer
-from runners.inference_server import InferencerServer
 
 @PytorchBootApplication("inference")
 class InferenceApp:
@@ -15,17 +14,3 @@ class InferenceApp:
                 Evaluator("path_to_your_eval_config").run()
         '''       
         Inferencer("./configs/local/inference_config.yaml").run()
-
-@PytorchBootApplication("server")
-class InferenceServerApp:
-    @staticmethod
-    def start():
-        ''' 
-            call default or your custom runners here, code will be executed 
-        automatically when type "pytorch-boot run" or "ptb run" in terminal
-        
-            example:
-                Trainer("path_to_your_train_config").run()
-                Evaluator("path_to_your_eval_config").run()
-        '''       
-        InferencerServer("./configs/server/server_inference_server_config.yaml").run()

beans/predict_result.py

@@ -1,162 +0,0 @@
-import numpy as np
-from sklearn.cluster import DBSCAN
-
-class PredictResult:
-    def __init__(self, raw_predict_result, input_pts=None, cluster_params=dict(eps=0.5, min_samples=2)):
-        self.input_pts = input_pts
-        self.cluster_params = cluster_params
-        self.sampled_9d_pose = raw_predict_result
-        self.sampled_matrix_pose = self.get_sampled_matrix_pose()
-        self.distance_matrix = self.calculate_distance_matrix()
-        self.clusters = self.get_cluster_result()
-        self.candidate_matrix_poses = self.get_candidate_poses()
-        self.candidate_9d_poses = [np.concatenate((self.matrix_to_rotation_6d_numpy(matrix[:3,:3]), matrix[:3,3].reshape(-1,)), axis=-1) for matrix in self.candidate_matrix_poses]
-        self.cluster_num = len(self.clusters)
-
-    @staticmethod
-    def rotation_6d_to_matrix_numpy(d6):
-        a1, a2 = d6[:3], d6[3:]
-        b1 = a1 / np.linalg.norm(a1)
-        b2 = a2 - np.dot(b1, a2) * b1
-        b2 = b2 / np.linalg.norm(b2)
-        b3 = np.cross(b1, b2)
-        return np.stack((b1, b2, b3), axis=-2)
-    
-    @staticmethod
-    def matrix_to_rotation_6d_numpy(matrix):
-        return np.copy(matrix[:2, :]).reshape((6,))
-    
-    def __str__(self):
-        info = "Predict Result:\n"
-        info += f"  Predicted pose number: {len(self.sampled_9d_pose)}\n"
-        info += f"  Cluster number: {self.cluster_num}\n"
-        for i, cluster in enumerate(self.clusters):
-            info += f"   - Cluster {i} size: {len(cluster)}\n"
-        max_distance = np.max(self.distance_matrix[self.distance_matrix != 0])
-        min_distance = np.min(self.distance_matrix[self.distance_matrix != 0])
-        info += f"  Max distance: {max_distance}\n"
-        info += f"  Min distance: {min_distance}\n"
-        return info
-    
-    def get_sampled_matrix_pose(self):
-        sampled_matrix_pose = []
-        for pose in self.sampled_9d_pose:
-            rotation = pose[:6]
-            translation = pose[6:]
-            pose = self.rotation_6d_to_matrix_numpy(rotation)
-            pose = np.concatenate((pose, translation.reshape(-1, 1)), axis=-1)
-            pose = np.concatenate((pose, np.array([[0, 0, 0, 1]])), axis=-2)
-            sampled_matrix_pose.append(pose)
-        return np.array(sampled_matrix_pose)
-
-    def rotation_distance(self, R1, R2):
-        R = np.dot(R1.T, R2)
-        trace = np.trace(R)
-        angle = np.arccos(np.clip((trace - 1) / 2, -1, 1))
-        return angle
-
-    def calculate_distance_matrix(self):
-        n = len(self.sampled_matrix_pose)
-        dist_matrix = np.zeros((n, n))
-        for i in range(n):
-            for j in range(n):
-                dist_matrix[i, j] = self.rotation_distance(self.sampled_matrix_pose[i][:3, :3], self.sampled_matrix_pose[j][:3, :3])
-        return dist_matrix
-
-    def cluster_rotations(self):
-        clustering = DBSCAN(eps=self.cluster_params['eps'], min_samples=self.cluster_params['min_samples'], metric='precomputed')
-        labels = clustering.fit_predict(self.distance_matrix)
-        return labels
-
-    def get_cluster_result(self):
-        labels = self.cluster_rotations()
-        cluster_num = len(set(labels)) - (1 if -1 in labels else 0)
-        clusters = []
-        for _ in range(cluster_num):
-            clusters.append([])
-        for matrix_pose, label in zip(self.sampled_matrix_pose, labels):
-            if label != -1:
-                clusters[label].append(matrix_pose)
-        clusters.sort(key=len, reverse=True)
-        return clusters
-
-    def get_center_matrix_pose_from_cluster(self, cluster):
-        min_total_distance = float('inf')
-        center_matrix_pose = None
-        
-        for matrix_pose in cluster:
-            total_distance = 0
-            for other_matrix_pose in cluster:
-                rot_distance = self.rotation_distance(matrix_pose[:3, :3], other_matrix_pose[:3, :3])
-                total_distance += rot_distance
-            
-            if total_distance < min_total_distance:
-                min_total_distance = total_distance
-                center_matrix_pose = matrix_pose
-        
-        return center_matrix_pose
-    
-    def get_candidate_poses(self):
-        candidate_poses = []
-        for cluster in self.clusters:
-            candidate_poses.append(self.get_center_matrix_pose_from_cluster(cluster))
-        return candidate_poses
-    
-    def visualize(self):
-        import plotly.graph_objects as go
-        fig = go.Figure()
-        if self.input_pts is not None:
-            fig.add_trace(go.Scatter3d(
-                x=self.input_pts[:, 0], y=self.input_pts[:, 1], z=self.input_pts[:, 2],
-                mode='markers', marker=dict(size=1, color='gray', opacity=0.5), name='Input Points'
-            ))
-        colors = ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
-             'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg']
-        for i, cluster in enumerate(self.clusters):
-            color = colors[i]
-            candidate_pose = self.candidate_matrix_poses[i]
-            origin_candidate = candidate_pose[:3, 3]
-            z_axis_candidate = candidate_pose[:3, 2]
-            for pose in cluster:
-                origin = pose[:3, 3]
-                z_axis = pose[:3, 2]
-                fig.add_trace(go.Cone(
-                    x=[origin[0]], y=[origin[1]], z=[origin[2]],
-                    u=[z_axis[0]], v=[z_axis[1]], w=[z_axis[2]],
-                    colorscale=color,
-                    sizemode="absolute", sizeref=0.05, anchor="tail", showscale=False
-                ))
-            fig.add_trace(go.Cone(
-                x=[origin_candidate[0]], y=[origin_candidate[1]], z=[origin_candidate[2]],
-                u=[z_axis_candidate[0]], v=[z_axis_candidate[1]], w=[z_axis_candidate[2]],
-                colorscale=color,
-                sizemode="absolute", sizeref=0.1, anchor="tail", showscale=False
-            ))
-        
-        fig.update_layout(
-            title="Clustered Poses and Input Points",
-            scene=dict(
-                xaxis_title='X',
-                yaxis_title='Y',
-                zaxis_title='Z'
-            ),
-            margin=dict(l=0, r=0, b=0, t=40),
-            scene_camera=dict(eye=dict(x=1.25, y=1.25, z=1.25))
-        )
-        
-        fig.show()
-
-
-
-if __name__ == "__main__":
-    step = 0
-    raw_predict_result = np.load(f"inference_result_pack/inference_result_pack/{step}/all_pred_pose_9d.npy")
-    input_pts = np.loadtxt(f"inference_result_pack/inference_result_pack/{step}/input_pts.txt")
-    print(raw_predict_result.shape)
-    predict_result = PredictResult(raw_predict_result, input_pts, cluster_params=dict(eps=0.25, min_samples=3))
-    print(predict_result)
-    print(len(predict_result.candidate_matrix_poses))
-    print(predict_result.distance_matrix)
-    #import ipdb; ipdb.set_trace()
-    predict_result.visualize()
-

configs/local/inference_config.yaml

@@ -1,76 +1,76 @@
 
 runner:
   general:
-    seed: 0
+    seed: 1
     device: cuda
     cuda_visible_devices: "0,1,2,3,4,5,6,7"
     
   experiment:
-    name: train_ab_global_only_dense
+    name: w_gf_wo_lf_full
     root_dir: "experiments"
-    epoch: 441 # -1 stands for last epoch
+    epoch: 1 # -1 stands for last epoch
 
   test:
     dataset_list:
-      - OmniObject3d_test
+      - OmniObject3d_train
 
   blender_script_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py"
-  output_dir: "/media/hofee/data/data/p++_dense"
+  output_dir: "/media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction/test/inference_global_full_on_testset"
-  pipeline: nbv_reconstruction_pipeline
+  pipeline: nbv_reconstruction_global_pts_pipeline
-  voxel_size: 0.003
+  
-  min_new_area: 1.0
 dataset:
-  # OmniObject3d_train:
+  OmniObject3d_train:
-  #   root_dir: "C:\\Document\\Datasets\\inference_test1"
+    root_dir: "/media/hofee/repository/nbv_reconstruction_data_512"
-  #   model_dir: "C:\\Document\\Datasets\\scaled_object_meshes"
-  #   source: seq_reconstruction_dataset_preprocessed
-  #   split_file: "C:\\Document\\Datasets\\data_list\\sample.txt"
-  #   type: test
-  #   filter_degree: 75
-  #   ratio: 1
-  #   batch_size: 1
-  #   num_workers: 12
-  #   pts_num: 8192
-  #   load_from_preprocess: True
-
-  OmniObject3d_test:
-    root_dir: "/media/hofee/data/data/new_testset_output"
     model_dir: "/media/hofee/data/data/scaled_object_meshes"
-    source: seq_reconstruction_dataset_preprocessed
+    source: seq_nbv_reconstruction_dataset
-    # split_file: "C:\\Document\\Datasets\\data_list\\OmniObject3d_test.txt"
+    split_file: "/media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction/test/test_set_list.txt"
     type: test
     filter_degree: 75
-    eval_list:
+    ratio: 1
-      - pose_diff
-      - coverage_rate_increase
-    ratio: 0.1
     batch_size: 1
     num_workers: 12
-    pts_num: 8192
+    pts_num: 4096
-    load_from_preprocess: True
+    load_from_preprocess: False
 
 pipeline:
-  nbv_reconstruction_pipeline:
+  nbv_reconstruction_local_pts_pipeline:
     modules:
-      pts_encoder: pointnet++_encoder
+      pts_encoder: pointnet_encoder
       seq_encoder: transformer_seq_encoder
       pose_encoder: pose_encoder
       view_finder: gf_view_finder
     eps: 1e-5
+    global_scanned_feat: False
+
+  nbv_reconstruction_global_pts_pipeline:
+    modules:
+      pts_encoder: pointnet_encoder
+      pose_seq_encoder: transformer_pose_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
     global_scanned_feat: True
 
+
+    
 module:
-  pointnet++_encoder:
-    in_dim: 3
-    params_name: light
 
   pointnet_encoder:
     in_dim: 3
     out_dim: 1024
     global_feat: True
     feature_transform: False
+
   transformer_seq_encoder:
-    embed_dim: 256
+    pts_embed_dim: 1024
+    pose_embed_dim: 256
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 2048
+
+  transformer_pose_seq_encoder:
+    pose_embed_dim: 256
     num_heads: 4
     ffn_dim: 256
     num_layers: 3
@@ -86,8 +86,7 @@ module:
     sample_mode: ode
     sampling_steps: 500
     sde_mode: ve
+
   pose_encoder:
     pose_dim: 9
     out_dim: 256
-  pts_num_encoder:
-    out_dim: 64

configs/local/strategy_generate_config.yaml

@@ -15,13 +15,13 @@ runner:
     overlap_area_threshold: 30
     compute_with_normal: False
     scan_points_threshold: 10
-    overwrite: False  
+    overwrite: False
     seq_num: 10
     dataset_list:
       - OmniObject3d
   
 datasets:
     OmniObject3d:
-      root_dir: /media/hofee/data/data/test_bottle/view 
+      root_dir: /data/hofee/nbv_rec_part2_preprocessed
-      from: 0
+      from: 155
-      to: -1 # ..-1 means end
+      to: 165 # ..-1 means end

configs/local/view_generate_config.yaml

@@ -8,11 +8,11 @@ runner:
     root_dir: experiments
   generate:
     port: 5002
-    from: 0
+    from: 600
-    to: 50 # -1 means all
+    to: -1 # -1 means all
-    object_dir: /media/hofee/data/data/test_bottle/bottle_mesh
+    object_dir: /media/hofee/data/data/object_meshes_part1
-    table_model_path: /media/hofee/data/data/others/table.obj
+    table_model_path: "/media/hofee/data/data/others/table.obj"
-    output_dir: /media/hofee/data/data/test_bottle/view
+    output_dir: /media/hofee/repository/data_part_1
     binocular_vision: true
     plane_size: 10
     max_views: 512
@@ -34,7 +34,7 @@ runner:
         max_y: 0.05
         min_z: 0.01
         max_z: 0.01
-        random_rotation_ratio: 0.0
+        random_rotation_ratio: 0.3
       random_objects:
         num: 4
         cluster: 0.9

configs/server/server_inference_server_config.yaml

@@ -1,53 +0,0 @@
-
-runner:
-  general:
-    seed: 0
-    device: cuda
-    cuda_visible_devices: "0,1,2,3,4,5,6,7"
-    
-  experiment:
-    name: train_ab_global_only
-    root_dir: "experiments"
-    epoch: -1 # -1 stands for last epoch
-
-  pipeline: nbv_reconstruction_pipeline
-  voxel_size: 0.003
-
-pipeline:
-  nbv_reconstruction_pipeline:
-    modules:
-      pts_encoder: pointnet_encoder
-      seq_encoder: transformer_seq_encoder
-      pose_encoder: pose_encoder
-      view_finder: gf_view_finder
-    eps: 1e-5
-    global_scanned_feat: True
-
-module:
-  pointnet_encoder:
-    in_dim: 3
-    out_dim: 1024
-    global_feat: True
-    feature_transform: False
-  transformer_seq_encoder:
-    embed_dim: 256
-    num_heads: 4
-    ffn_dim: 256
-    num_layers: 3
-    output_dim: 1024
-
-  gf_view_finder:
-    t_feat_dim: 128
-    pose_feat_dim: 256
-    main_feat_dim: 2048
-    regression_head: Rx_Ry_and_T
-    pose_mode: rot_matrix
-    per_point_feature: False
-    sample_mode: ode
-    sampling_steps: 500
-    sde_mode: ve
-  pose_encoder:
-    pose_dim: 9
-    out_dim: 256
-  pts_num_encoder:
-    out_dim: 64

configs/server/server_split_dataset_config.yaml

@@ -6,17 +6,17 @@ runner:
     cuda_visible_devices: "0,1,2,3,4,5,6,7"
     
   experiment:
-    name: debug
+    name: server_split_dataset
     root_dir: "experiments"
   
   split: # 
-    root_dir: "/data/hofee/data/packed_preprocessed_data"
+    root_dir: "/data/hofee/data/new_full_data"
     type: "unseen_instance" # "unseen_category"
     datasets:
       OmniObject3d_train: 
-        path: "/data/hofee/data/OmniObject3d_train.txt"
+        path: "/data/hofee/data/new_full_data_list/OmniObject3d_train.txt"
         ratio: 0.9
 
       OmniObject3d_test: 
-        path: "/data/hofee/data/OmniObject3d_test.txt"
+        path: "/data/hofee/data/new_full_data_list/OmniObject3d_test.txt"
         ratio: 0.1

configs/server/server_train_config.yaml

@@ -3,11 +3,11 @@ runner:
   general:
     seed: 0
     device: cuda
-    cuda_visible_devices: "0"
+    cuda_visible_devices: "1"
     parallel: False
     
   experiment:
-    name: train_ab_global_only_with_wp_p++_strong
+    name: train_ab_global_and_partial_global
     root_dir: "experiments"
     use_checkpoint: False
     epoch: -1 # -1 stands for last epoch
@@ -19,13 +19,13 @@ runner:
     optimizer:
       type: Adam
       lr: 0.0001
-    losses:   
+    losses: 
       - gf_loss
     dataset: OmniObject3d_train
   test:
     frequency: 3 # test frequency
     dataset_list:
-      - OmniObject3d_test
+      #- OmniObject3d_test
       - OmniObject3d_val
 
   pipeline: nbv_reconstruction_pipeline
@@ -39,7 +39,7 @@ dataset:
     type: train
     cache: True
     ratio: 1
-    batch_size: 64
+    batch_size: 80
     num_workers: 128
     pts_num: 8192
     load_from_preprocess: True
@@ -80,7 +80,7 @@ dataset:
 pipeline:
   nbv_reconstruction_pipeline:
     modules:
-      pts_encoder: pointnet++_encoder
+      pts_encoder: pointnet_encoder
       seq_encoder: transformer_seq_encoder
       pose_encoder: pose_encoder
       view_finder: gf_view_finder
@@ -96,12 +96,8 @@ module:
     global_feat: True
     feature_transform: False
 
-  pointnet++_encoder:
-    in_dim: 3
-    params_name: strong
-
   transformer_seq_encoder:
-    embed_dim: 256
+    embed_dim: 320
     num_heads: 4
     ffn_dim: 256
     num_layers: 3
@@ -110,7 +106,7 @@ module:
   gf_view_finder:
     t_feat_dim: 128
     pose_feat_dim: 256
-    main_feat_dim: 5120
+    main_feat_dim: 2048
     regression_head: Rx_Ry_and_T
     pose_mode: rot_matrix
     per_point_feature: False

core/nbv_dataset.py

@@ -4,10 +4,10 @@ import PytorchBoot.namespace as namespace
 import PytorchBoot.stereotype as stereotype
 from PytorchBoot.config import ConfigManager
 from PytorchBoot.utils.log_util import Log
-
 import torch
 import os
 import sys
+import time
 
 sys.path.append(r"/data/hofee/project/nbv_rec/nbv_reconstruction")
 
@@ -51,7 +51,7 @@ class NBVReconstructionDataset(BaseDataset):
                 scene_name_list.append(scene_name)
         return scene_name_list
 
-    def get_datalist(self, bias=False):
+    def get_datalist(self):
         datalist = []
         for scene_name in self.scene_name_list:
             seq_num = DataLoadUtil.get_label_num(self.root_dir, scene_name)
@@ -80,18 +80,16 @@ class NBVReconstructionDataset(BaseDataset):
                     for data_pair in label_data["data_pairs"]:
                         scanned_views = data_pair[0]
                         next_best_view = data_pair[1]
-                        accept_probability = scanned_views[-1][1]
+                        datalist.append(
-                        if accept_probability > np.random.rand():
+                            {
-                            datalist.append(
+                                "scanned_views": scanned_views,
-                                {
+                                "next_best_view": next_best_view,
-                                    "scanned_views": scanned_views,
+                                "seq_max_coverage_rate": max_coverage_rate,
-                                    "next_best_view": next_best_view,
+                                "scene_name": scene_name,
-                                    "seq_max_coverage_rate": max_coverage_rate,
+                                "label_idx": seq_idx,
-                                    "scene_name": scene_name,
+                                "scene_max_coverage_rate": scene_max_coverage_rate,
-                                    "label_idx": seq_idx,
+                            }
-                                    "scene_max_coverage_rate": scene_max_coverage_rate,
+                        )
-                                }
-                            )
         return datalist
 
     def preprocess_cache(self):
@@ -117,8 +115,13 @@ class NBVReconstructionDataset(BaseDataset):
         except Exception as e:
             Log.error(f"Save cache failed: {e}")
 
-    def voxel_downsample_with_mask(self, pts, voxel_size):
+    def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
-        pass
+        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
+        unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
+        idx_sort = np.argsort(inverse)
+        idx_unique = idx_sort[np.cumsum(counts)-counts]
+        downsampled_points = point_cloud[idx_unique]
+        return downsampled_points, inverse
 
 
     def __getitem__(self, index):
@@ -132,6 +135,9 @@ class NBVReconstructionDataset(BaseDataset):
             scanned_coverages_rate,
             scanned_n_to_world_pose,
         ) = ([], [], [])
+        start_time = time.time()
+        start_indices = [0]
+        total_points = 0
         for view in scanned_views:
             frame_idx = view[0]
             coverage_rate = view[1]
@@ -153,8 +159,12 @@ class NBVReconstructionDataset(BaseDataset):
             n_to_world_trans = n_to_world_pose[:3, 3]
             n_to_world_9d = np.concatenate([n_to_world_6d, n_to_world_trans], axis=0)
             scanned_n_to_world_pose.append(n_to_world_9d)
+            total_points += len(downsampled_target_point_cloud)
+            start_indices.append(total_points)
 
 
+        end_time = time.time()
+        #Log.info(f"load data time: {end_time - start_time}")
         nbv_idx, nbv_coverage_rate = nbv[0], nbv[1]
         nbv_path = DataLoadUtil.get_path(self.root_dir, scene_name, nbv_idx)
         cam_info = DataLoadUtil.load_cam_info(nbv_path)
@@ -167,14 +177,27 @@ class NBVReconstructionDataset(BaseDataset):
         best_to_world_9d = np.concatenate(
             [best_to_world_6d, best_to_world_trans], axis=0
         )
-
-        combined_scanned_views_pts = np.concatenate(scanned_views_pts, axis=0)
-        voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_views_pts, 0.002)
-        random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, self.pts_num)
         
+        combined_scanned_views_pts = np.concatenate(scanned_views_pts, axis=0)
+        voxel_downsampled_combined_scanned_pts_np, inverse = self.voxel_downsample_with_mapping(combined_scanned_views_pts, 0.003)
+        random_downsampled_combined_scanned_pts_np, random_downsample_idx = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, self.pts_num, require_idx=True)
+
+        all_idx_unique = np.arange(len(voxel_downsampled_combined_scanned_pts_np))
+        all_random_downsample_idx = all_idx_unique[random_downsample_idx]
+        scanned_pts_mask = []
+        for idx, start_idx in enumerate(start_indices):
+            if idx == len(start_indices) - 1:
+                break
+            end_idx = start_indices[idx+1]
+            view_inverse = inverse[start_idx:end_idx]
+            view_unique_downsampled_idx = np.unique(view_inverse)
+            view_unique_downsampled_idx_set = set(view_unique_downsampled_idx)
+            mask = np.array([idx in view_unique_downsampled_idx_set for idx in all_random_downsample_idx])
+            scanned_pts_mask.append(mask)
         data_item = {
             "scanned_pts": np.asarray(scanned_views_pts, dtype=np.float32), # Ndarray(S x Nv x 3)
             "combined_scanned_pts": np.asarray(random_downsampled_combined_scanned_pts_np, dtype=np.float32), # Ndarray(N x 3)
+            "scanned_pts_mask": np.asarray(scanned_pts_mask, dtype=np.bool), # Ndarray(N)
             "scanned_coverage_rate": scanned_coverages_rate, # List(S): Float, range(0, 1)
             "scanned_n_to_world_pose_9d": np.asarray(scanned_n_to_world_pose, dtype=np.float32), # Ndarray(S x 9)
             "best_coverage_rate": nbv_coverage_rate, # Float, range(0, 1)
@@ -200,7 +223,9 @@ class NBVReconstructionDataset(BaseDataset):
             collate_data["scanned_n_to_world_pose_9d"] = [
                 torch.tensor(item["scanned_n_to_world_pose_9d"]) for item in batch
             ]
-            
+            collate_data["scanned_pts_mask"] = [
+                torch.tensor(item["scanned_pts_mask"]) for item in batch
+            ]
             ''' ------ Fixed Length ------ '''
             
             collate_data["best_to_world_pose_9d"] = torch.stack(
@@ -209,12 +234,14 @@ class NBVReconstructionDataset(BaseDataset):
             collate_data["combined_scanned_pts"] = torch.stack(
                 [torch.tensor(item["combined_scanned_pts"]) for item in batch]
             )
+            
             for key in batch[0].keys():
                 if key not in [
                     "scanned_pts",
                     "scanned_n_to_world_pose_9d",
                     "best_to_world_pose_9d",
                     "combined_scanned_pts",
+                    "scanned_pts_mask",
                 ]:
                     collate_data[key] = [item[key] for item in batch]
             return collate_data
@@ -230,10 +257,9 @@ if __name__ == "__main__":
     torch.manual_seed(seed)
     np.random.seed(seed)
     config = {
-        "root_dir": "/data/hofee/data/new_full_data",
+        "root_dir": "/data/hofee/nbv_rec_part2_preprocessed",
-        "model_dir": "../data/scaled_object_meshes",
         "source": "nbv_reconstruction_dataset",
-        "split_file": "/data/hofee/data/new_full_data_list/OmniObject3d_train.txt",
+        "split_file": "/data/hofee/data/sample.txt",
         "load_from_preprocess": True,
         "ratio": 0.5,
         "batch_size": 2,

core/old_seq_dataset.py

@@ -1,154 +0,0 @@
-import numpy as np
-from PytorchBoot.dataset import BaseDataset
-import PytorchBoot.namespace as namespace
-import PytorchBoot.stereotype as stereotype
-from PytorchBoot.utils.log_util import Log
-import torch
-import os
-import sys
-sys.path.append(r"/home/data/hofee/project/nbv_rec/nbv_reconstruction")
-
-from utils.data_load import DataLoadUtil
-from utils.pose import PoseUtil
-from utils.pts import PtsUtil
-
-@stereotype.dataset("old_seq_nbv_reconstruction_dataset")
-class SeqNBVReconstructionDataset(BaseDataset):
-    def __init__(self, config):
-        super(SeqNBVReconstructionDataset, self).__init__(config)
-        self.type = config["type"]
-        if self.type != namespace.Mode.TEST:
-            Log.error("Dataset <seq_nbv_reconstruction_dataset> Only support test mode", terminate=True)
-        self.config = config
-        self.root_dir = config["root_dir"]
-        self.split_file_path = config["split_file"]
-        self.scene_name_list = self.load_scene_name_list()
-        self.datalist = self.get_datalist()
-        self.pts_num = config["pts_num"]
-        
-        self.model_dir = config["model_dir"]
-        self.filter_degree = config["filter_degree"]
-        self.load_from_preprocess = config.get("load_from_preprocess", False)
-        
-
-    def load_scene_name_list(self):
-        scene_name_list = []
-        with open(self.split_file_path, "r") as f:
-            for line in f:
-                scene_name = line.strip()
-                scene_name_list.append(scene_name)
-        return scene_name_list
-    
-    def get_datalist(self):
-        datalist = []
-        for scene_name in self.scene_name_list:
-            seq_num = DataLoadUtil.get_label_num(self.root_dir, scene_name)
-            scene_max_coverage_rate = 0
-            scene_max_cr_idx = 0
-
-            for seq_idx in range(seq_num):
-                label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, seq_idx)
-                label_data = DataLoadUtil.load_label(label_path)
-                max_coverage_rate = label_data["max_coverage_rate"]
-                if max_coverage_rate > scene_max_coverage_rate:
-                    scene_max_coverage_rate = max_coverage_rate
-                    scene_max_cr_idx = seq_idx
-        
-            label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, scene_max_cr_idx)
-            label_data = DataLoadUtil.load_label(label_path)
-            first_frame = label_data["best_sequence"][0]
-            best_seq_len = len(label_data["best_sequence"])
-            datalist.append({
-                    "scene_name": scene_name,
-                    "first_frame": first_frame,
-                    "max_coverage_rate": scene_max_coverage_rate,
-                    "best_seq_len": best_seq_len,
-                    "label_idx": scene_max_cr_idx,
-                })
-        return datalist
-
-    def __getitem__(self, index):
-        data_item_info = self.datalist[index]
-        first_frame_idx = data_item_info["first_frame"][0]
-        first_frame_coverage = data_item_info["first_frame"][1]
-        max_coverage_rate = data_item_info["max_coverage_rate"]
-        scene_name = data_item_info["scene_name"]
-        first_cam_info = DataLoadUtil.load_cam_info(DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx), binocular=True)
-        first_view_path = DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx)
-        first_left_cam_pose = first_cam_info["cam_to_world"]
-        first_center_cam_pose = first_cam_info["cam_to_world_O"]
-        first_target_point_cloud = DataLoadUtil.load_from_preprocessed_pts(first_view_path)
-        first_pts_num = first_target_point_cloud.shape[0]
-        first_downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(first_target_point_cloud, self.pts_num)
-        first_to_world_rot_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(first_left_cam_pose[:3,:3]))
-        first_to_world_trans = first_left_cam_pose[:3,3]
-        first_to_world_9d = np.concatenate([first_to_world_rot_6d, first_to_world_trans], axis=0)
-        diag = DataLoadUtil.get_bbox_diag(self.model_dir, scene_name)
-        voxel_threshold = diag*0.02
-        first_O_to_first_L_pose = np.dot(np.linalg.inv(first_left_cam_pose), first_center_cam_pose)
-        scene_path = os.path.join(self.root_dir, scene_name)
-        model_points_normals = DataLoadUtil.load_points_normals(self.root_dir, scene_name)
-
-        data_item = {
-            "first_pts_num": np.asarray(
-                first_pts_num, dtype=np.int32
-            ),
-            "first_pts": np.asarray([first_downsampled_target_point_cloud],dtype=np.float32),
-            "combined_scanned_pts": np.asarray(first_downsampled_target_point_cloud,dtype=np.float32),
-            "first_to_world_9d": np.asarray([first_to_world_9d],dtype=np.float32),
-            "scene_name": scene_name,
-            "max_coverage_rate": max_coverage_rate,
-            "voxel_threshold": voxel_threshold,
-            "filter_degree": self.filter_degree,
-            "O_to_L_pose": first_O_to_first_L_pose,
-            "first_frame_coverage": first_frame_coverage,
-            "scene_path": scene_path,
-            "model_points_normals": model_points_normals,
-            "best_seq_len": data_item_info["best_seq_len"],
-            "first_frame_id": first_frame_idx,
-        }
-        return data_item
-
-    def __len__(self):
-        return len(self.datalist)
-    
-    def get_collate_fn(self):
-        def collate_fn(batch):
-            collate_data = {}
-            collate_data["first_pts"] = [torch.tensor(item['first_pts']) for item in batch]
-            collate_data["first_to_world_9d"] = [torch.tensor(item['first_to_world_9d']) for item in batch]
-            collate_data["combined_scanned_pts"] = torch.stack([torch.tensor(item['combined_scanned_pts']) for item in batch])
-            for key in batch[0].keys():
-                if key not in ["first_pts", "first_to_world_9d", "combined_scanned_pts"]:
-                    collate_data[key] = [item[key] for item in batch]
-            return collate_data
-        return collate_fn
-
-# -------------- Debug ---------------- #
-if __name__ == "__main__":
-    import torch
-    seed = 0
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    config = {
-        "root_dir": "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy",
-        "split_file": "/home/data/hofee/project/nbv_rec/data/OmniObject3d_train.txt",
-        "model_dir": "/home/data/hofee/project/nbv_rec/data/scaled_object_meshes",
-        "ratio": 0.005,
-        "batch_size": 2,
-        "filter_degree": 75,
-        "num_workers": 0,
-        "pts_num": 32684,
-        "type": namespace.Mode.TEST,
-        "load_from_preprocess": True
-    }
-    ds = SeqNBVReconstructionDataset(config)
-    print(len(ds))
-    #ds.__getitem__(10)
-    dl = ds.get_loader(shuffle=True)
-    for idx, data in enumerate(dl):
-        data = ds.process_batch(data, "cuda:0")
-        print(data)
-        # ------  Debug Start ------
-        import ipdb;ipdb.set_trace()
-        # ------  Debug End ------+

core/pipeline.py

@@ -75,8 +75,6 @@ class NBVReconstructionPipeline(nn.Module):
 
     def forward_test(self, data):
         main_feat = self.get_main_feat(data)
-        repeat_num = data.get("repeat_num", 1)
-        main_feat = main_feat.repeat(repeat_num, 1)
         estimated_delta_rot_9d, in_process_sample = self.view_finder.next_best_view(
             main_feat
         )
@@ -91,25 +89,49 @@ class NBVReconstructionPipeline(nn.Module):
             "scanned_n_to_world_pose_9d"
         ]  # List(B): Tensor(S x 9)
 
+        scanned_pts_mask_batch = data["scanned_pts_mask"] # List(B): Tensor(N)
+
         device = next(self.parameters()).device
 
         embedding_list_batch = []
 
         combined_scanned_pts_batch = data["combined_scanned_pts"]  # Tensor(B x N x 3)
-        global_scanned_feat = self.pts_encoder.encode_points(
+        global_scanned_feat, per_point_feat_batch = self.pts_encoder.encode_points(
-            combined_scanned_pts_batch, require_per_point_feat=False
+            combined_scanned_pts_batch, require_per_point_feat=True
         )  # global_scanned_feat: Tensor(B x Dg)
-
+        batch_size = len(scanned_n_to_world_pose_9d_batch)
-        for scanned_n_to_world_pose_9d in scanned_n_to_world_pose_9d_batch:
+        for i in range(batch_size):
-            scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)  # Tensor(S x 9)
+            seq_len = len(scanned_n_to_world_pose_9d_batch[i])
+            scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d_batch[i].to(device)  # Tensor(S x 9)
+            scanned_pts_mask = scanned_pts_mask_batch[i] # Tensor(S x N)
+            per_point_feat = per_point_feat_batch[i] # Tensor(N x Dp)
+            partial_point_feat_seq = []
+            for j in range(seq_len):
+                partial_per_point_feat = per_point_feat[scanned_pts_mask[j]]
+                if partial_per_point_feat.shape[0] == 0:
+                    partial_point_feat = torch.zeros(per_point_feat.shape[1], device=device)
+                else:
+                    partial_point_feat = torch.mean(partial_per_point_feat, dim=0) # Tensor(Dp)
+                partial_point_feat_seq.append(partial_point_feat)
+            partial_point_feat_seq = torch.stack(partial_point_feat_seq, dim=0) # Tensor(S x Dp)
+            
             pose_feat_seq = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)  # Tensor(S x Dp) 
-            seq_embedding = pose_feat_seq
+
+            seq_embedding = torch.cat([partial_point_feat_seq, pose_feat_seq], dim=-1)
+            
             embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp))
         
         seq_feat = self.seq_encoder.encode_sequence(embedding_list_batch) # Tensor(B x Ds)
         main_feat = torch.cat([seq_feat, global_scanned_feat], dim=-1) # Tensor(B x (Ds+Dg))
 
         if torch.isnan(main_feat).any():
+            for i in range(len(main_feat)):
+                if torch.isnan(main_feat[i]).any():
+                    scanned_pts_mask = scanned_pts_mask_batch[i]
+                    Log.info(f"scanned_pts_mask shape: {scanned_pts_mask.shape}")
+                    Log.info(f"scanned_pts_mask sum: {scanned_pts_mask.sum()}")
+                    import ipdb
+                    ipdb.set_trace()
             Log.error("nan in main_feat", True)
 
         return main_feat

core/seq_dataset.py

@@ -1,209 +1,154 @@
-import numpy as np
+import numpy as np
-from PytorchBoot.dataset import BaseDataset
+from PytorchBoot.dataset import BaseDataset
-import PytorchBoot.namespace as namespace
+import PytorchBoot.namespace as namespace
-import PytorchBoot.stereotype as stereotype
+import PytorchBoot.stereotype as stereotype
-from PytorchBoot.config import ConfigManager
+from PytorchBoot.utils.log_util import Log
-from PytorchBoot.utils.log_util import Log
+import torch
-import torch
+import os
-import os
+import sys
-import sys
+sys.path.append(r"/home/data/hofee/project/nbv_rec/nbv_reconstruction")
-
+
-sys.path.append(r"/media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction")
+from utils.data_load import DataLoadUtil
-
+from utils.pose import PoseUtil
-from utils.data_load import DataLoadUtil
+from utils.pts import PtsUtil
-from utils.pose import PoseUtil
+
-from utils.pts import PtsUtil
+@stereotype.dataset("seq_nbv_reconstruction_dataset")
-
+class SeqNBVReconstructionDataset(BaseDataset):
-
+    def __init__(self, config):
-@stereotype.dataset("seq_reconstruction_dataset")
+        super(SeqNBVReconstructionDataset, self).__init__(config)
-class SeqReconstructionDataset(BaseDataset):
+        self.type = config["type"]
-    def __init__(self, config):
+        if self.type != namespace.Mode.TEST:
-        super(SeqReconstructionDataset, self).__init__(config)
+            Log.error("Dataset <seq_nbv_reconstruction_dataset> Only support test mode", terminate=True)
-        self.config = config
+        self.config = config
-        self.root_dir = config["root_dir"]
+        self.root_dir = config["root_dir"]
-        self.split_file_path = config["split_file"]
+        self.split_file_path = config["split_file"]
-        self.scene_name_list = self.load_scene_name_list()
+        self.scene_name_list = self.load_scene_name_list()
-        self.datalist = self.get_datalist()
+        self.datalist = self.get_datalist()
-
+        self.pts_num = config["pts_num"]
-        self.pts_num = config["pts_num"]
+        
-        self.type = config["type"]
+        self.model_dir = config["model_dir"]
-        self.cache = config.get("cache")
+        self.filter_degree = config["filter_degree"]
-        self.load_from_preprocess = config.get("load_from_preprocess", False)
+        self.load_from_preprocess = config.get("load_from_preprocess", False)
-
+        
-        if self.type == namespace.Mode.TEST:
+
-            #self.model_dir = config["model_dir"]
+    def load_scene_name_list(self):
-            self.filter_degree = config["filter_degree"]
+        scene_name_list = []
-        if self.type == namespace.Mode.TRAIN:
+        with open(self.split_file_path, "r") as f:
-            scale_ratio = 1
+            for line in f:
-            self.datalist = self.datalist*scale_ratio
+                scene_name = line.strip()
-        if self.cache:
+                scene_name_list.append(scene_name)
-            expr_root = ConfigManager.get("runner", "experiment", "root_dir")
+        return scene_name_list
-            expr_name = ConfigManager.get("runner", "experiment", "name")
+    
-            self.cache_dir = os.path.join(expr_root, expr_name, "cache")
+    def get_datalist(self):
-            # self.preprocess_cache()
+        datalist = []
-
+        for scene_name in self.scene_name_list:
-    def load_scene_name_list(self):
+            seq_num = DataLoadUtil.get_label_num(self.root_dir, scene_name)
-        scene_name_list = []
+            scene_max_coverage_rate = 0
-        with open(self.split_file_path, "r") as f:
+            scene_max_cr_idx = 0
-            for line in f:
+
-                scene_name = line.strip()
+            for seq_idx in range(seq_num):
-                if not os.path.exists(os.path.join(self.root_dir, scene_name)):
+                label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, seq_idx)
-                    continue
+                label_data = DataLoadUtil.load_label(label_path)
-                scene_name_list.append(scene_name)
+                max_coverage_rate = label_data["max_coverage_rate"]
-        return scene_name_list    
+                if max_coverage_rate > scene_max_coverage_rate:
-    
+                    scene_max_coverage_rate = max_coverage_rate
-    def get_scene_name_list(self):
+                    scene_max_cr_idx = seq_idx
-        return self.scene_name_list
+        
-
+            label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, scene_max_cr_idx)
-
+            label_data = DataLoadUtil.load_label(label_path)
-    def get_datalist(self):
+            first_frame = label_data["best_sequence"][0]
-        datalist = []
+            best_seq_len = len(label_data["best_sequence"])
-        total = len(self.scene_name_list)
+            datalist.append({
-        for idx, scene_name in enumerate(self.scene_name_list):
+                    "scene_name": scene_name,
-            print(f"processing {scene_name} ({idx}/{total})")
+                    "first_frame": first_frame,
-            scene_max_cr_idx = 0
+                    "max_coverage_rate": scene_max_coverage_rate,
-            frame_len = DataLoadUtil.get_scene_seq_length(self.root_dir, scene_name)
+                    "best_seq_len": best_seq_len,
-
+                    "label_idx": scene_max_cr_idx,
-            for i in range(10,frame_len):
+                })
-                path = DataLoadUtil.get_path(self.root_dir, scene_name, i)
+        return datalist
-                pts = DataLoadUtil.load_from_preprocessed_pts(path, "npy")
+
-                print(pts.shape)
+    def __getitem__(self, index):
-                if pts.shape[0] == 0:
+        data_item_info = self.datalist[index]
-                    continue
+        first_frame_idx = data_item_info["first_frame"][0]
-                else:
+        first_frame_coverage = data_item_info["first_frame"][1]
-                    break
+        max_coverage_rate = data_item_info["max_coverage_rate"]
-            print(i)
+        scene_name = data_item_info["scene_name"]
-            datalist.append({
+        first_cam_info = DataLoadUtil.load_cam_info(DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx), binocular=True)
-                "scene_name": scene_name,
+        first_view_path = DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx)
-                "first_frame": i,
+        first_left_cam_pose = first_cam_info["cam_to_world"]
-                "best_seq_len": -1,
+        first_center_cam_pose = first_cam_info["cam_to_world_O"]
-                "max_coverage_rate": 1.0,
+        first_target_point_cloud = DataLoadUtil.load_from_preprocessed_pts(first_view_path)
-                "label_idx": scene_max_cr_idx,
+        first_pts_num = first_target_point_cloud.shape[0]
-            })  
+        first_downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(first_target_point_cloud, self.pts_num)
-        return datalist
+        first_to_world_rot_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(first_left_cam_pose[:3,:3]))
-
+        first_to_world_trans = first_left_cam_pose[:3,3]
-    def preprocess_cache(self):
+        first_to_world_9d = np.concatenate([first_to_world_rot_6d, first_to_world_trans], axis=0)
-        Log.info("preprocessing cache...")
+        diag = DataLoadUtil.get_bbox_diag(self.model_dir, scene_name)
-        for item_idx in range(len(self.datalist)):
+        voxel_threshold = diag*0.02
-            self.__getitem__(item_idx)
+        first_O_to_first_L_pose = np.dot(np.linalg.inv(first_left_cam_pose), first_center_cam_pose)
-        Log.success("finish preprocessing cache.")
+        scene_path = os.path.join(self.root_dir, scene_name)
-
+        model_points_normals = DataLoadUtil.load_points_normals(self.root_dir, scene_name)
-    def load_from_cache(self, scene_name, curr_frame_idx):
+
-        cache_name = f"{scene_name}_{curr_frame_idx}.txt"
+        data_item = {
-        cache_path = os.path.join(self.cache_dir, cache_name)
+            "first_pts_num": np.asarray(
-        if os.path.exists(cache_path):
+                first_pts_num, dtype=np.int32
-            data = np.loadtxt(cache_path)
+            ),
-            return data
+            "first_pts": np.asarray([first_downsampled_target_point_cloud],dtype=np.float32),
-        else:
+            "combined_scanned_pts": np.asarray(first_downsampled_target_point_cloud,dtype=np.float32),
-            return None
+            "first_to_world_9d": np.asarray([first_to_world_9d],dtype=np.float32),
-
+            "scene_name": scene_name,
-    def save_to_cache(self, scene_name, curr_frame_idx, data):
+            "max_coverage_rate": max_coverage_rate,
-        cache_name = f"{scene_name}_{curr_frame_idx}.txt"
+            "voxel_threshold": voxel_threshold,
-        cache_path = os.path.join(self.cache_dir, cache_name)
+            "filter_degree": self.filter_degree,
-        try:
+            "O_to_L_pose": first_O_to_first_L_pose,
-            np.savetxt(cache_path, data)
+            "first_frame_coverage": first_frame_coverage,
-        except Exception as e:
+            "scene_path": scene_path,
-            Log.error(f"Save cache failed: {e}")
+            "model_points_normals": model_points_normals,
-
+            "best_seq_len": data_item_info["best_seq_len"],
-    def seq_combined_pts(self, scene, frame_idx_list):
+            "first_frame_id": first_frame_idx,
-        all_combined_pts = []   
+        }
-        for i in frame_idx_list:
+        return data_item
-            path = DataLoadUtil.get_path(self.root_dir, scene, i)
+
-            pts = DataLoadUtil.load_from_preprocessed_pts(path,"npy")
+    def __len__(self):
-            if pts.shape[0] == 0:
+        return len(self.datalist)
-                continue
+    
-            all_combined_pts.append(pts)
+    def get_collate_fn(self):
-        all_combined_pts = np.vstack(all_combined_pts)
+        def collate_fn(batch):
-        downsampled_all_pts = PtsUtil.voxel_downsample_point_cloud(all_combined_pts, 0.003)
+            collate_data = {}
-        return downsampled_all_pts
+            collate_data["first_pts"] = [torch.tensor(item['first_pts']) for item in batch]
-
+            collate_data["first_to_world_9d"] = [torch.tensor(item['first_to_world_9d']) for item in batch]
-    def __getitem__(self, index):
+            collate_data["combined_scanned_pts"] = torch.stack([torch.tensor(item['combined_scanned_pts']) for item in batch])
-        data_item_info = self.datalist[index]
+            for key in batch[0].keys():
-        max_coverage_rate = data_item_info["max_coverage_rate"]
+                if key not in ["first_pts", "first_to_world_9d", "combined_scanned_pts"]:
-        best_seq_len = data_item_info["best_seq_len"]
+                    collate_data[key] = [item[key] for item in batch]
-        scene_name = data_item_info["scene_name"]
+            return collate_data
-        (
+        return collate_fn
-            scanned_views_pts,
+
-            scanned_coverages_rate,
+# -------------- Debug ---------------- #
-            scanned_n_to_world_pose,
+if __name__ == "__main__":
-        ) = ([], [], [])
+    import torch
-        view = data_item_info["first_frame"]
+    seed = 0
-        frame_idx = view
+    torch.manual_seed(seed)
-        view_path = DataLoadUtil.get_path(self.root_dir, scene_name, frame_idx)
+    np.random.seed(seed)
-        cam_info = DataLoadUtil.load_cam_info(view_path, binocular=True)
+    config = {
-        
+        "root_dir": "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy",
-        n_to_world_pose = cam_info["cam_to_world"]  
+        "split_file": "/home/data/hofee/project/nbv_rec/data/OmniObject3d_train.txt",
-        target_point_cloud = (
+        "model_dir": "/home/data/hofee/project/nbv_rec/data/scaled_object_meshes",
-            DataLoadUtil.load_from_preprocessed_pts(view_path)
+        "ratio": 0.005,
-        )
+        "batch_size": 2,
-        downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(
+        "filter_degree": 75,
-            target_point_cloud, self.pts_num
+        "num_workers": 0,
-        )
+        "pts_num": 32684,
-        scanned_views_pts.append(downsampled_target_point_cloud)
+        "type": namespace.Mode.TEST,
-        
+        "load_from_preprocess": True
-        n_to_world_6d = PoseUtil.matrix_to_rotation_6d_numpy(
+    }
-            np.asarray(n_to_world_pose[:3, :3])
+    ds = SeqNBVReconstructionDataset(config)
-        ) 
+    print(len(ds))
-        first_left_cam_pose = cam_info["cam_to_world"]
+    #ds.__getitem__(10)
-        first_center_cam_pose = cam_info["cam_to_world_O"]
+    dl = ds.get_loader(shuffle=True)
-        first_O_to_first_L_pose = np.dot(np.linalg.inv(first_left_cam_pose), first_center_cam_pose)
+    for idx, data in enumerate(dl):
-        n_to_world_trans = n_to_world_pose[:3, 3]
+        data = ds.process_batch(data, "cuda:0")
-        n_to_world_9d = np.concatenate([n_to_world_6d, n_to_world_trans], axis=0)
+        print(data)
-        scanned_n_to_world_pose.append(n_to_world_9d)
+        # ------  Debug Start ------
-
+        import ipdb;ipdb.set_trace()
-        frame_list = []
+        # ------  Debug End ------+
-        for i in range(DataLoadUtil.get_scene_seq_length(self.root_dir, scene_name)):
-            frame_list.append(i)
-        gt_pts = self.seq_combined_pts(scene_name, frame_list)
-        data_item = {
-            "first_scanned_pts": np.asarray(scanned_views_pts, dtype=np.float32), # Ndarray(S x Nv x 3)
-            "first_scanned_n_to_world_pose_9d": np.asarray(scanned_n_to_world_pose, dtype=np.float32), # Ndarray(S x 9)
-            "seq_max_coverage_rate": max_coverage_rate, # Float, range(0, 1)
-            "best_seq_len": best_seq_len, # Int
-            "scene_name": scene_name, # String
-            "gt_pts": gt_pts, # Ndarray(N x 3)
-            "scene_path": os.path.join(self.root_dir, scene_name), # String
-            "O_to_L_pose": first_O_to_first_L_pose,
-        }
-
-        return data_item
-
-    def __len__(self):
-        return len(self.datalist)
-
-
-# -------------- Debug ---------------- #
-if __name__ == "__main__":
-    import torch
-    from tqdm import tqdm
-    import pickle
-    import os
-
-    seed = 0
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    
-    config = {
-        "root_dir": "/media/hofee/data/data/test_bottle/view",
-        "source": "seq_reconstruction_dataset",
-        "split_file": "/media/hofee/data/data/test_bottle/test_bottle.txt",
-        "load_from_preprocess": True,
-        "filter_degree": 75,
-        "num_workers": 0,
-        "pts_num": 8192,
-        "type": namespace.Mode.TEST,
-    }
-    
-    output_dir = "/media/hofee/data/data/test_bottle/preprocessed_dataset"
-    os.makedirs(output_dir, exist_ok=True)
-
-    ds = SeqReconstructionDataset(config)
-    for i in tqdm(range(len(ds)), desc="processing dataset"):
-        output_path = os.path.join(output_dir, f"item_{i}.pkl")
-        item = ds.__getitem__(i)
-        for key, value in item.items():
-            if isinstance(value, np.ndarray):
-                item[key] = value.tolist()
-        #import ipdb; ipdb.set_trace()
-        with open(output_path, "wb") as f:
-            pickle.dump(item, f)

core/seq_dataset_preprocessed.py

@@ -1,82 +0,0 @@
-import numpy as np
-from PytorchBoot.dataset import BaseDataset
-import PytorchBoot.namespace as namespace
-import PytorchBoot.stereotype as stereotype
-from PytorchBoot.config import ConfigManager
-from PytorchBoot.utils.log_util import Log
-import pickle
-import torch
-import os
-import sys
-
-sys.path.append(r"C:\Document\Local Project\nbv_rec\nbv_reconstruction")
-
-from utils.data_load import DataLoadUtil
-from utils.pose import PoseUtil
-from utils.pts import PtsUtil
-
-@stereotype.dataset("seq_reconstruction_dataset_preprocessed")
-class SeqReconstructionDatasetPreprocessed(BaseDataset):
-    def __init__(self, config):
-        super(SeqReconstructionDatasetPreprocessed, self).__init__(config)
-        self.config = config
-        self.root_dir = config["root_dir"]
-        self.real_root_dir = r"/media/hofee/data/data/new_testset"
-        self.item_list = os.listdir(self.root_dir)
-
-    def __getitem__(self, index):
-        data = pickle.load(open(os.path.join(self.root_dir, self.item_list[index]), "rb"))
-        data_item = {
-            "first_scanned_pts": np.asarray(data["first_scanned_pts"], dtype=np.float32), # Ndarray(S x Nv x 3)
-            "first_scanned_n_to_world_pose_9d": np.asarray(data["first_scanned_n_to_world_pose_9d"], dtype=np.float32), # Ndarray(S x 9)
-            "seq_max_coverage_rate": data["seq_max_coverage_rate"], # Float, range(0, 1)
-            "best_seq_len": data["best_seq_len"], # Int
-            "scene_name": data["scene_name"], # String
-            "gt_pts": np.asarray(data["gt_pts"], dtype=np.float32), # Ndarray(N x 3)
-            "scene_path": os.path.join(self.real_root_dir, data["scene_name"]), # String
-            "O_to_L_pose": np.asarray(data["O_to_L_pose"], dtype=np.float32),
-        }
-        return data_item
-
-    def __len__(self):
-        return len(self.item_list)
-
-# -------------- Debug ---------------- #
-if __name__ == "__main__":
-    import torch
-
-    seed = 0
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    '''
-    OmniObject3d_test:
-    root_dir: "H:\\AI\\Datasets\\packed_test_data"
-    model_dir: "H:\\AI\\Datasets\\scaled_object_meshes"
-    source: seq_reconstruction_dataset
-    split_file: "H:\\AI\\Datasets\\data_list\\OmniObject3d_test.txt"
-    type: test
-    filter_degree: 75
-    eval_list:
-      - pose_diff
-      - coverage_rate_increase
-    ratio: 0.1
-    batch_size: 1
-    num_workers: 12
-    pts_num: 8192
-    load_from_preprocess: True
-    '''
-    config = {
-        "root_dir": "/media/hofee/data/data/test_bottle/preprocessed_dataset",
-        "source": "seq_reconstruction_dataset",
-        "split_file": "H:\\AI\\Datasets\\data_list\\OmniObject3d_test.txt",
-        "load_from_preprocess": True,
-        "ratio": 1,
-        "filter_degree": 75,
-        "num_workers": 0,
-        "pts_num": 8192,
-        "type": "test",
-    }
-    ds = SeqReconstructionDataset(config)
-    print(len(ds))
-    print(ds.__getitem__(10))
-    

modules/module_lib/pointnet2_modules.py

@@ -1,162 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from . import pointnet2_utils
-from . import pytorch_utils as pt_utils
-from typing import List
-
-
-class _PointnetSAModuleBase(nn.Module):
-
-    def __init__(self):
-        super().__init__()
-        self.npoint = None
-        self.groupers = None
-        self.mlps = None
-        self.pool_method = 'max_pool'
-
-    def forward(self, xyz: torch.Tensor, features: torch.Tensor = None, new_xyz=None) -> (torch.Tensor, torch.Tensor):
-        """
-        :param xyz: (B, N, 3) tensor of the xyz coordinates of the features
-        :param features: (B, N, C) tensor of the descriptors of the the features
-        :param new_xyz:
-        :return:
-            new_xyz: (B, npoint, 3) tensor of the new features' xyz
-            new_features: (B, npoint, \sum_k(mlps[k][-1])) tensor of the new_features descriptors
-        """
-        new_features_list = []
-
-        xyz_flipped = xyz.transpose(1, 2).contiguous()
-        if new_xyz is None:
-            new_xyz = pointnet2_utils.gather_operation(
-                xyz_flipped,
-                pointnet2_utils.furthest_point_sample(xyz, self.npoint)
-            ).transpose(1, 2).contiguous() if self.npoint is not None else None
-
-        for i in range(len(self.groupers)):
-            new_features = self.groupers[i](xyz, new_xyz, features)  # (B, C, npoint, nsample)
-
-            new_features = self.mlps[i](new_features)  # (B, mlp[-1], npoint, nsample)
-
-            if self.pool_method == 'max_pool':
-                new_features = F.max_pool2d(
-                    new_features, kernel_size=[1, new_features.size(3)]
-                )  # (B, mlp[-1], npoint, 1)
-            elif self.pool_method == 'avg_pool':
-                new_features = F.avg_pool2d(
-                    new_features, kernel_size=[1, new_features.size(3)]
-                )  # (B, mlp[-1], npoint, 1)
-            else:
-                raise NotImplementedError
-
-            new_features = new_features.squeeze(-1)  # (B, mlp[-1], npoint)
-            new_features_list.append(new_features)
-
-        return new_xyz, torch.cat(new_features_list, dim=1)
-
-
-class PointnetSAModuleMSG(_PointnetSAModuleBase):
-    """Pointnet set abstraction layer with multiscale grouping"""
-
-    def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True,
-                 use_xyz: bool = True, pool_method='max_pool', instance_norm=False):
-        """
-        :param npoint: int
-        :param radii: list of float, list of radii to group with
-        :param nsamples: list of int, number of samples in each ball query
-        :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale
-        :param bn: whether to use batchnorm
-        :param use_xyz:
-        :param pool_method: max_pool / avg_pool
-        :param instance_norm: whether to use instance_norm
-        """
-        super().__init__()
-
-        assert len(radii) == len(nsamples) == len(mlps)
-
-        self.npoint = npoint
-        self.groupers = nn.ModuleList()
-        self.mlps = nn.ModuleList()
-        for i in range(len(radii)):
-            radius = radii[i]
-            nsample = nsamples[i]
-            self.groupers.append(
-                pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
-                if npoint is not None else pointnet2_utils.GroupAll(use_xyz)
-            )
-            mlp_spec = mlps[i]
-            if use_xyz:
-                mlp_spec[0] += 3
-
-            self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm))
-        self.pool_method = pool_method
-
-
-class PointnetSAModule(PointnetSAModuleMSG):
-    """Pointnet set abstraction layer"""
-
-    def __init__(self, *, mlp: List[int], npoint: int = None, radius: float = None, nsample: int = None,
-                 bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False):
-        """
-        :param mlp: list of int, spec of the pointnet before the global max_pool
-        :param npoint: int, number of features
-        :param radius: float, radius of ball
-        :param nsample: int, number of samples in the ball query
-        :param bn: whether to use batchnorm
-        :param use_xyz:
-        :param pool_method: max_pool / avg_pool
-        :param instance_norm: whether to use instance_norm
-        """
-        super().__init__(
-            mlps=[mlp], npoint=npoint, radii=[radius], nsamples=[nsample], bn=bn, use_xyz=use_xyz,
-            pool_method=pool_method, instance_norm=instance_norm
-        )
-
-
-class PointnetFPModule(nn.Module):
-    r"""Propigates the features of one set to another"""
-
-    def __init__(self, *, mlp: List[int], bn: bool = True):
-        """
-        :param mlp: list of int
-        :param bn: whether to use batchnorm
-        """
-        super().__init__()
-        self.mlp = pt_utils.SharedMLP(mlp, bn=bn)
-
-    def forward(
-            self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor
-    ) -> torch.Tensor:
-        """
-        :param unknown: (B, n, 3) tensor of the xyz positions of the unknown features
-        :param known: (B, m, 3) tensor of the xyz positions of the known features
-        :param unknow_feats: (B, C1, n) tensor of the features to be propigated to
-        :param known_feats: (B, C2, m) tensor of features to be propigated
-        :return:
-            new_features: (B, mlp[-1], n) tensor of the features of the unknown features
-        """
-        if known is not None:
-            dist, idx = pointnet2_utils.three_nn(unknown, known)
-            dist_recip = 1.0 / (dist + 1e-8)
-            norm = torch.sum(dist_recip, dim=2, keepdim=True)
-            weight = dist_recip / norm
-
-            interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight)
-        else:
-            interpolated_feats = known_feats.expand(*known_feats.size()[0:2], unknown.size(1))
-
-        if unknow_feats is not None:
-            new_features = torch.cat([interpolated_feats, unknow_feats], dim=1)  # (B, C2 + C1, n)
-        else:
-            new_features = interpolated_feats
-
-        new_features = new_features.unsqueeze(-1)
-
-        new_features = self.mlp(new_features)
-
-        return new_features.squeeze(-1)
-
-
-if __name__ == "__main__":
-    pass

modules/module_lib/pointnet2_utils.py

@@ -1,291 +0,0 @@
-import torch
-from torch.autograd import Variable
-from torch.autograd import Function
-import torch.nn as nn
-from typing import Tuple
-import sys
-
-import pointnet2_cuda as pointnet2
-
-
-class FurthestPointSampling(Function):
-    @staticmethod
-    def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor:
-        """
-        Uses iterative furthest point sampling to select a set of npoint features that have the largest
-        minimum distance
-        :param ctx:
-        :param xyz: (B, N, 3) where N > npoint
-        :param npoint: int, number of features in the sampled set
-        :return:
-             output: (B, npoint) tensor containing the set
-        """
-        assert xyz.is_contiguous()
-
-        B, N, _ = xyz.size()
-        output = torch.cuda.IntTensor(B, npoint)
-        temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
-
-        pointnet2.furthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
-        return output
-
-    @staticmethod
-    def backward(xyz, a=None):
-        return None, None
-
-
-furthest_point_sample = FurthestPointSampling.apply
-
-
-class GatherOperation(Function):
-
-    @staticmethod
-    def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
-        """
-        :param ctx:
-        :param features: (B, C, N)
-        :param idx: (B, npoint) index tensor of the features to gather
-        :return:
-            output: (B, C, npoint)
-        """
-        assert features.is_contiguous()
-        assert idx.is_contiguous()
-
-        B, npoint = idx.size()
-        _, C, N = features.size()
-        output = torch.cuda.FloatTensor(B, C, npoint)
-
-        pointnet2.gather_points_wrapper(B, C, N, npoint, features, idx, output)
-
-        ctx.for_backwards = (idx, C, N)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        idx, C, N = ctx.for_backwards
-        B, npoint = idx.size()
-
-        grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
-        grad_out_data = grad_out.data.contiguous()
-        pointnet2.gather_points_grad_wrapper(B, C, N, npoint, grad_out_data, idx, grad_features.data)
-        return grad_features, None
-
-
-gather_operation = GatherOperation.apply
-
-
-class ThreeNN(Function):
-
-    @staticmethod
-    def forward(ctx, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Find the three nearest neighbors of unknown in known
-        :param ctx:
-        :param unknown: (B, N, 3)
-        :param known: (B, M, 3)
-        :return:
-            dist: (B, N, 3) l2 distance to the three nearest neighbors
-            idx: (B, N, 3) index of 3 nearest neighbors
-        """
-        assert unknown.is_contiguous()
-        assert known.is_contiguous()
-
-        B, N, _ = unknown.size()
-        m = known.size(1)
-        dist2 = torch.cuda.FloatTensor(B, N, 3)
-        idx = torch.cuda.IntTensor(B, N, 3)
-
-        pointnet2.three_nn_wrapper(B, N, m, unknown, known, dist2, idx)
-        return torch.sqrt(dist2), idx
-
-    @staticmethod
-    def backward(ctx, a=None, b=None):
-        return None, None
-
-
-three_nn = ThreeNN.apply
-
-
-class ThreeInterpolate(Function):
-
-    @staticmethod
-    def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
-        """
-        Performs weight linear interpolation on 3 features
-        :param ctx:
-        :param features: (B, C, M) Features descriptors to be interpolated from
-        :param idx: (B, n, 3) three nearest neighbors of the target features in features
-        :param weight: (B, n, 3) weights
-        :return:
-            output: (B, C, N) tensor of the interpolated features
-        """
-        assert features.is_contiguous()
-        assert idx.is_contiguous()
-        assert weight.is_contiguous()
-
-        B, c, m = features.size()
-        n = idx.size(1)
-        ctx.three_interpolate_for_backward = (idx, weight, m)
-        output = torch.cuda.FloatTensor(B, c, n)
-
-        pointnet2.three_interpolate_wrapper(B, c, m, n, features, idx, weight, output)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        :param ctx:
-        :param grad_out: (B, C, N) tensor with gradients of outputs
-        :return:
-            grad_features: (B, C, M) tensor with gradients of features
-            None:
-            None:
-        """
-        idx, weight, m = ctx.three_interpolate_for_backward
-        B, c, n = grad_out.size()
-
-        grad_features = Variable(torch.cuda.FloatTensor(B, c, m).zero_())
-        grad_out_data = grad_out.data.contiguous()
-
-        pointnet2.three_interpolate_grad_wrapper(B, c, n, m, grad_out_data, idx, weight, grad_features.data)
-        return grad_features, None, None
-
-
-three_interpolate = ThreeInterpolate.apply
-
-
-class GroupingOperation(Function):
-
-    @staticmethod
-    def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
-        """
-        :param ctx:
-        :param features: (B, C, N) tensor of features to group
-        :param idx: (B, npoint, nsample) tensor containing the indicies of features to group with
-        :return:
-            output: (B, C, npoint, nsample) tensor
-        """
-        assert features.is_contiguous()
-        assert idx.is_contiguous()
-
-        B, nfeatures, nsample = idx.size()
-        _, C, N = features.size()
-        output = torch.cuda.FloatTensor(B, C, nfeatures, nsample)
-
-        pointnet2.group_points_wrapper(B, C, N, nfeatures, nsample, features, idx, output)
-
-        ctx.for_backwards = (idx, N)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        :param ctx:
-        :param grad_out: (B, C, npoint, nsample) tensor of the gradients of the output from forward
-        :return:
-            grad_features: (B, C, N) gradient of the features
-        """
-        idx, N = ctx.for_backwards
-
-        B, C, npoint, nsample = grad_out.size()
-        grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
-
-        grad_out_data = grad_out.data.contiguous()
-        pointnet2.group_points_grad_wrapper(B, C, N, npoint, nsample, grad_out_data, idx, grad_features.data)
-        return grad_features, None
-
-
-grouping_operation = GroupingOperation.apply
-
-
-class BallQuery(Function):
-
-    @staticmethod
-    def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor) -> torch.Tensor:
-        """
-        :param ctx:
-        :param radius: float, radius of the balls
-        :param nsample: int, maximum number of features in the balls
-        :param xyz: (B, N, 3) xyz coordinates of the features
-        :param new_xyz: (B, npoint, 3) centers of the ball query
-        :return:
-            idx: (B, npoint, nsample) tensor with the indicies of the features that form the query balls
-        """
-        assert new_xyz.is_contiguous()
-        assert xyz.is_contiguous()
-
-        B, N, _ = xyz.size()
-        npoint = new_xyz.size(1)
-        idx = torch.cuda.IntTensor(B, npoint, nsample).zero_()
-
-        pointnet2.ball_query_wrapper(B, N, npoint, radius, nsample, new_xyz, xyz, idx)
-        return idx
-
-    @staticmethod
-    def backward(ctx, a=None):
-        return None, None, None, None
-
-
-ball_query = BallQuery.apply
-
-
-class QueryAndGroup(nn.Module):
-    def __init__(self, radius: float, nsample: int, use_xyz: bool = True):
-        """
-        :param radius: float, radius of ball
-        :param nsample: int, maximum number of features to gather in the ball
-        :param use_xyz:
-        """
-        super().__init__()
-        self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz
-
-    def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None) -> Tuple[torch.Tensor]:
-        """
-        :param xyz: (B, N, 3) xyz coordinates of the features
-        :param new_xyz: (B, npoint, 3) centroids
-        :param features: (B, C, N) descriptors of the features
-        :return:
-            new_features: (B, 3 + C, npoint, nsample)
-        """
-        idx = ball_query(self.radius, self.nsample, xyz, new_xyz)
-        xyz_trans = xyz.transpose(1, 2).contiguous()
-        grouped_xyz = grouping_operation(xyz_trans, idx)  # (B, 3, npoint, nsample)
-        grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1)
-
-        if features is not None:
-            grouped_features = grouping_operation(features, idx)
-            if self.use_xyz:
-                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (B, C + 3, npoint, nsample)
-            else:
-                new_features = grouped_features
-        else:
-            assert self.use_xyz, "Cannot have not features and not use xyz as a feature!"
-            new_features = grouped_xyz
-
-        return new_features
-
-
-class GroupAll(nn.Module):
-    def __init__(self, use_xyz: bool = True):
-        super().__init__()
-        self.use_xyz = use_xyz
-
-    def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None):
-        """
-        :param xyz: (B, N, 3) xyz coordinates of the features
-        :param new_xyz: ignored
-        :param features: (B, C, N) descriptors of the features
-        :return:
-            new_features: (B, C + 3, 1, N)
-        """
-        grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
-        if features is not None:
-            grouped_features = features.unsqueeze(2)
-            if self.use_xyz:
-                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (B, 3 + C, 1, N)
-            else:
-                new_features = grouped_features
-        else:
-            new_features = grouped_xyz
-
-        return new_features

modules/module_lib/pytorch_utils.py

@@ -1,236 +0,0 @@
-import torch.nn as nn
-from typing import List, Tuple
-
-
-class SharedMLP(nn.Sequential):
-
-    def __init__(
-            self,
-            args: List[int],
-            *,
-            bn: bool = False,
-            activation=nn.ReLU(inplace=True),
-            preact: bool = False,
-            first: bool = False,
-            name: str = "",
-            instance_norm: bool = False,
-    ):
-        super().__init__()
-
-        for i in range(len(args) - 1):
-            self.add_module(
-                name + 'layer{}'.format(i),
-                Conv2d(
-                    args[i],
-                    args[i + 1],
-                    bn=(not first or not preact or (i != 0)) and bn,
-                    activation=activation
-                    if (not first or not preact or (i != 0)) else None,
-                    preact=preact,
-                    instance_norm=instance_norm
-                )
-            )
-
-
-class _ConvBase(nn.Sequential):
-
-    def __init__(
-            self,
-            in_size,
-            out_size,
-            kernel_size,
-            stride,
-            padding,
-            activation,
-            bn,
-            init,
-            conv=None,
-            batch_norm=None,
-            bias=True,
-            preact=False,
-            name="",
-            instance_norm=False,
-            instance_norm_func=None
-    ):
-        super().__init__()
-
-        bias = bias and (not bn)
-        conv_unit = conv(
-            in_size,
-            out_size,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            bias=bias
-        )
-        init(conv_unit.weight)
-        if bias:
-            nn.init.constant_(conv_unit.bias, 0)
-
-        if bn:
-            if not preact:
-                bn_unit = batch_norm(out_size)
-            else:
-                bn_unit = batch_norm(in_size)
-        if instance_norm:
-            if not preact:
-                in_unit = instance_norm_func(out_size, affine=False, track_running_stats=False)
-            else:
-                in_unit = instance_norm_func(in_size, affine=False, track_running_stats=False)
-
-        if preact:
-            if bn:
-                self.add_module(name + 'bn', bn_unit)
-
-            if activation is not None:
-                self.add_module(name + 'activation', activation)
-
-            if not bn and instance_norm:
-                self.add_module(name + 'in', in_unit)
-
-        self.add_module(name + 'conv', conv_unit)
-
-        if not preact:
-            if bn:
-                self.add_module(name + 'bn', bn_unit)
-
-            if activation is not None:
-                self.add_module(name + 'activation', activation)
-
-            if not bn and instance_norm:
-                self.add_module(name + 'in', in_unit)
-
-
-class _BNBase(nn.Sequential):
-
-    def __init__(self, in_size, batch_norm=None, name=""):
-        super().__init__()
-        self.add_module(name + "bn", batch_norm(in_size))
-
-        nn.init.constant_(self[0].weight, 1.0)
-        nn.init.constant_(self[0].bias, 0)
-
-
-class BatchNorm1d(_BNBase):
-
-    def __init__(self, in_size: int, *, name: str = ""):
-        super().__init__(in_size, batch_norm=nn.BatchNorm1d, name=name)
-
-
-class BatchNorm2d(_BNBase):
-
-    def __init__(self, in_size: int, name: str = ""):
-        super().__init__(in_size, batch_norm=nn.BatchNorm2d, name=name)
-
-
-class Conv1d(_ConvBase):
-
-    def __init__(
-            self,
-            in_size: int,
-            out_size: int,
-            *,
-            kernel_size: int = 1,
-            stride: int = 1,
-            padding: int = 0,
-            activation=nn.ReLU(inplace=True),
-            bn: bool = False,
-            init=nn.init.kaiming_normal_,
-            bias: bool = True,
-            preact: bool = False,
-            name: str = "",
-            instance_norm=False
-    ):
-        super().__init__(
-            in_size,
-            out_size,
-            kernel_size,
-            stride,
-            padding,
-            activation,
-            bn,
-            init,
-            conv=nn.Conv1d,
-            batch_norm=BatchNorm1d,
-            bias=bias,
-            preact=preact,
-            name=name,
-            instance_norm=instance_norm,
-            instance_norm_func=nn.InstanceNorm1d
-        )
-
-
-class Conv2d(_ConvBase):
-
-    def __init__(
-            self,
-            in_size: int,
-            out_size: int,
-            *,
-            kernel_size: Tuple[int, int] = (1, 1),
-            stride: Tuple[int, int] = (1, 1),
-            padding: Tuple[int, int] = (0, 0),
-            activation=nn.ReLU(inplace=True),
-            bn: bool = False,
-            init=nn.init.kaiming_normal_,
-            bias: bool = True,
-            preact: bool = False,
-            name: str = "",
-            instance_norm=False
-    ):
-        super().__init__(
-            in_size,
-            out_size,
-            kernel_size,
-            stride,
-            padding,
-            activation,
-            bn,
-            init,
-            conv=nn.Conv2d,
-            batch_norm=BatchNorm2d,
-            bias=bias,
-            preact=preact,
-            name=name,
-            instance_norm=instance_norm,
-            instance_norm_func=nn.InstanceNorm2d
-        )
-
-
-class FC(nn.Sequential):
-
-    def __init__(
-            self,
-            in_size: int,
-            out_size: int,
-            *,
-            activation=nn.ReLU(inplace=True),
-            bn: bool = False,
-            init=None,
-            preact: bool = False,
-            name: str = ""
-    ):
-        super().__init__()
-
-        fc = nn.Linear(in_size, out_size, bias=not bn)
-        if init is not None:
-            init(fc.weight)
-        if not bn:
-            nn.init.constant(fc.bias, 0)
-
-        if preact:
-            if bn:
-                self.add_module(name + 'bn', BatchNorm1d(in_size))
-
-            if activation is not None:
-                self.add_module(name + 'activation', activation)
-
-        self.add_module(name + 'fc', fc)
-
-        if not preact:
-            if bn:
-                self.add_module(name + 'bn', BatchNorm1d(out_size))
-
-            if activation is not None:
-                self.add_module(name + 'activation', activation)
-

modules/pointnet++_encoder.py

@@ -1,148 +0,0 @@
-import torch
-import torch.nn as nn
-import os
-import sys
-path = os.path.abspath(__file__)
-for i in range(2):
-    path = os.path.dirname(path)
-PROJECT_ROOT = path                 
-sys.path.append(PROJECT_ROOT)
-import PytorchBoot.stereotype as stereotype
-from modules.module_lib.pointnet2_modules import PointnetSAModuleMSG
-
-
-ClsMSG_CFG_Dense = {
-    'NPOINTS': [512, 256, 128, None],
-    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
-    'NSAMPLE': [[32, 64], [16, 32], [8, 16], [None, None]],
-    'MLPS': [[[16, 16, 32], [32, 32, 64]],
-             [[64, 64, 128], [64, 96, 128]],
-             [[128, 196, 256], [128, 196, 256]],
-             [[256, 256, 512], [256, 384, 512]]],
-    'DP_RATIO': 0.5,
-}
-
-ClsMSG_CFG_Light = {
-    'NPOINTS': [512, 256, 128, None],
-    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
-    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [None, None]],
-    'MLPS': [[[16, 16, 32], [32, 32, 64]],
-             [[64, 64, 128], [64, 96, 128]],
-             [[128, 196, 256], [128, 196, 256]],
-             [[256, 256, 512], [256, 384, 512]]],
-    'DP_RATIO': 0.5,
-}
-
-ClsMSG_CFG_Light_2048 = {
-    'NPOINTS': [512, 256, 128, None],
-    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
-    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [None, None]],
-    'MLPS': [[[16, 16, 32], [32, 32, 64]],
-         [[64, 64, 128], [64, 96, 128]],
-         [[128, 196, 256], [128, 196, 256]],
-         [[256, 256, 1024], [256, 512, 1024]]],
-    'DP_RATIO': 0.5,
-}
-
-ClsMSG_CFG_Strong = {
-    'NPOINTS': [1024, 512, 256, 128, None],  # 增加采样点，获取更多细节
-    'RADIUS': [[0.02, 0.05], [0.05, 0.1], [0.1, 0.2], [0.2, 0.4], [None, None]],  # 增大感受野
-    'NSAMPLE': [[32, 64], [32, 64], [32, 64], [32, 64], [None, None]],  # 提高每层的采样点数
-    'MLPS': [[[32, 32, 64], [64, 64, 128]],  # 增强 MLP 层，增加特征提取能力
-             [[128, 128, 256], [128, 128, 256]],
-             [[256, 256, 512], [256, 384, 512]],
-             [[512, 512, 1024], [512, 768, 1024]],
-             [[1024, 1024, 2048], [1024, 1024, 2048]]],  # 增加更深的特征层
-    'DP_RATIO': 0.4,  # Dropout 比率稍微降低，以保留更多信息
-}
-
-ClsMSG_CFG_Lighter = {
-    'NPOINTS': [512, 256, 128, 64, None],
-    'RADIUS': [[0.01], [0.02], [0.04], [0.08], [None]],
-    'NSAMPLE': [[64], [32], [16], [8], [None]],
-    'MLPS': [[[32, 32, 64]],
-             [[64, 64, 128]],
-             [[128, 196, 256]],
-             [[256, 256, 512]],
-             [[512, 512, 1024]]],
-    'DP_RATIO': 0.5,
-}
-
-
-def select_params(name):
-    if name == 'light':
-        return ClsMSG_CFG_Light
-    elif name == 'lighter':
-        return ClsMSG_CFG_Lighter
-    elif name == 'dense':
-        return ClsMSG_CFG_Dense
-    elif name == 'light_2048':
-        return ClsMSG_CFG_Light_2048
-    elif name == 'strong':
-        return ClsMSG_CFG_Strong
-    else:
-        raise NotImplementedError
-
-
-def break_up_pc(pc):
-    xyz = pc[..., 0:3].contiguous()
-    features = (
-        pc[..., 3:].transpose(1, 2).contiguous()
-        if pc.size(-1) > 3 else None
-    )
-
-    return xyz, features
-
-
-@stereotype.module("pointnet++_encoder")
-class PointNet2Encoder(nn.Module):
-    def encode_points(self, pts, require_per_point_feat=False):
-        return self.forward(pts)
-
-    def __init__(self, config:dict):
-        super().__init__()
-
-        channel_in = config.get("in_dim", 3) - 3
-        params_name = config.get("params_name", "light")
-
-        self.SA_modules = nn.ModuleList()
-        selected_params = select_params(params_name)
-        for k in range(selected_params['NPOINTS'].__len__()):
-            mlps = selected_params['MLPS'][k].copy()
-            channel_out = 0
-            for idx in range(mlps.__len__()):
-                mlps[idx] = [channel_in] + mlps[idx]
-                channel_out += mlps[idx][-1]
-
-            self.SA_modules.append(
-                PointnetSAModuleMSG(
-                    npoint=selected_params['NPOINTS'][k],
-                    radii=selected_params['RADIUS'][k],
-                    nsamples=selected_params['NSAMPLE'][k],
-                    mlps=mlps,
-                    use_xyz=True,
-                    bn=True
-                )
-            )
-            channel_in = channel_out
-
-    def forward(self, point_cloud: torch.cuda.FloatTensor):
-        xyz, features = break_up_pc(point_cloud)
-
-        l_xyz, l_features = [xyz], [features]
-        for i in range(len(self.SA_modules)):
-            li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i])
-            l_xyz.append(li_xyz)
-            l_features.append(li_features)
-        return l_features[-1].squeeze(-1)
-
-
-if __name__ == '__main__':
-    seed = 100
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    net = PointNet2Encoder(config={"in_dim": 3, "params_name": "strong"}).cuda()
-    pts = torch.randn(2, 2444, 3).cuda()
-    print(torch.mean(pts, dim=1))
-    pre = net.encode_points(pts)
-    print(pre.shape)

preprocess/preprocessor.py

@@ -164,10 +164,10 @@ def save_scene_data(root, scene, scene_idx=0, scene_total=1,file_type="txt"):
 
 if __name__ == "__main__":
     #root = "/media/hofee/repository/new_data_with_normal"
-    root = r"/media/hofee/data/data/test_bottle/view"
+    root = r"H:\AI\Datasets\nbv_rec_part2"
     scene_list = os.listdir(root)
     from_idx = 0 # 1000
-    to_idx = len(scene_list) # 1500
+    to_idx = 600 # 1500
 
 
     cnt = 0

runners/inferece_server.py

@@ -12,9 +12,8 @@ from PytorchBoot.runners.runner import Runner
 from PytorchBoot.utils import Log
 
 from utils.pts import PtsUtil
-from beans.predict_result import PredictResult
 
-@stereotype.runner("inferencer_server")
+@stereotype.runner("inferencer")
 class InferencerServer(Runner):
     def __init__(self, config_path):
         super().__init__(config_path)
@@ -25,45 +24,40 @@ class InferencerServer(Runner):
         self.pipeline_name = self.config[namespace.Stereotype.PIPELINE]
         self.pipeline:torch.nn.Module = ComponentFactory.create(namespace.Stereotype.PIPELINE, self.pipeline_name)
         self.pipeline = self.pipeline.to(self.device)
-        self.pts_num = 8192
-        self.voxel_size = 0.002
             
         ''' Experiment '''
-        self.load_experiment("inferencer_server")
+        self.load_experiment("nbv_evaluator")
 
     def get_input_data(self, data):
         input_data = {}
         scanned_pts = data["scanned_pts"]
         scanned_n_to_world_pose_9d = data["scanned_n_to_world_pose_9d"]
         combined_scanned_views_pts = np.concatenate(scanned_pts, axis=0)
-        voxel_downsampled_combined_scanned_pts = PtsUtil.voxel_downsample_point_cloud(
-            combined_scanned_views_pts, self.voxel_size
-        )
         fps_downsampled_combined_scanned_pts, fps_idx = PtsUtil.fps_downsample_point_cloud(
-            voxel_downsampled_combined_scanned_pts, self.pts_num, require_idx=True
+            combined_scanned_views_pts, self.pts_num, require_idx=True
         )
+        combined_scanned_views_pts_mask = np.zeros(len(scanned_pts), dtype=np.uint8)
+        start_idx = 0
+        for i in range(len(scanned_pts)):
+            end_idx = start_idx + len(scanned_pts[i])
+            combined_scanned_views_pts_mask[start_idx:end_idx] = i
+            start_idx = end_idx
 
-        input_data["scanned_pts"] = scanned_pts
+        fps_downsampled_combined_scanned_pts_mask = combined_scanned_views_pts_mask[fps_idx]
+
+        input_data["scanned_pts_mask"] = np.asarray(fps_downsampled_combined_scanned_pts_mask, dtype=np.uint8)
         input_data["scanned_n_to_world_pose_9d"] = np.asarray(scanned_n_to_world_pose_9d, dtype=np.float32)
         input_data["combined_scanned_pts"] = np.asarray(fps_downsampled_combined_scanned_pts, dtype=np.float32)
         return input_data
     
     def get_result(self, output_data):
 
-        pred_pose_9d = output_data["pred_pose_9d"]
+        estimated_delta_rot_9d = output_data["pred_pose_9d"]
-        pred_pose_9d = np.asarray(PredictResult(pred_pose_9d.cpu().numpy(), None, cluster_params=dict(eps=0.25, min_samples=3)).candidate_9d_poses, dtype=np.float32)
         result = {
-            "pred_pose_9d": pred_pose_9d.tolist()
+            "estimated_delta_rot_9d": estimated_delta_rot_9d.tolist()
         }
         return result
     
-    def collate_input(self, input_data):
-        collated_input_data = {}
-        collated_input_data["scanned_pts"] = [torch.tensor(input_data["scanned_pts"], dtype=torch.float32, device=self.device)]
-        collated_input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(input_data["scanned_n_to_world_pose_9d"], dtype=torch.float32, device=self.device)]
-        collated_input_data["combined_scanned_pts"] = torch.tensor(input_data["combined_scanned_pts"], dtype=torch.float32, device=self.device).unsqueeze(0)
-        return collated_input_data
-    
     def run(self):
         Log.info("Loading from epoch {}.".format(self.current_epoch))
     
@@ -71,8 +65,7 @@ class InferencerServer(Runner):
         def inference():
             data = request.json
             input_data = self.get_input_data(data)
-            collated_input_data = self.collate_input(input_data)
+            output_data = self.pipeline.forward_test(input_data)
-            output_data = self.pipeline.forward_test(collated_input_data)
             result = self.get_result(output_data)
             return jsonify(result)
         

runners/inferencer.py

@@ -4,7 +4,6 @@ from utils.render import RenderUtil
 from utils.pose import PoseUtil
 from utils.pts import PtsUtil
 from utils.reconstruction import ReconstructionUtil
-from beans.predict_result import PredictResult
 
 import torch
 from tqdm import tqdm
@@ -20,19 +19,14 @@ from PytorchBoot.dataset import BaseDataset
 from PytorchBoot.runners.runner import Runner
 from PytorchBoot.utils import Log
 from PytorchBoot.status import status_manager
-from utils.data_load import DataLoadUtil
+
 @stereotype.runner("inferencer")
 class Inferencer(Runner):
     def __init__(self, config_path):
-        
         super().__init__(config_path)
     
         self.script_path = ConfigManager.get(namespace.Stereotype.RUNNER, "blender_script_path")
         self.output_dir = ConfigManager.get(namespace.Stereotype.RUNNER, "output_dir")
-        self.voxel_size = ConfigManager.get(namespace.Stereotype.RUNNER, "voxel_size")
-        self.min_new_area = ConfigManager.get(namespace.Stereotype.RUNNER, "min_new_area")
-        CM = 0.01
-        self.min_new_pts_num = self.min_new_area * (CM / self.voxel_size) **2
         ''' Pipeline '''
         self.pipeline_name = self.config[namespace.Stereotype.PIPELINE]
         self.pipeline:torch.nn.Module = ComponentFactory.create(namespace.Stereotype.PIPELINE, self.pipeline_name)
@@ -40,12 +34,7 @@ class Inferencer(Runner):
             
         ''' Experiment '''
         self.load_experiment("nbv_evaluator")
-        self.stat_result_path = os.path.join(self.output_dir, "stat.json")
+        self.stat_result = {}
-        if os.path.exists(self.stat_result_path):
-            with open(self.stat_result_path, "r") as f:
-                self.stat_result = json.load(f)
-        else:
-            self.stat_result = {}
 
         ''' Test '''
         self.test_config = ConfigManager.get(namespace.Stereotype.RUNNER, namespace.Mode.TEST)
@@ -76,182 +65,128 @@ class Inferencer(Runner):
             for dataset_idx, test_set in enumerate(self.test_set_list):
                 status_manager.set_progress("inference", "inferencer", f"dataset", dataset_idx, len(self.test_set_list))
                 test_set_name = test_set.get_name()
+                test_loader = test_set.get_loader()
 
-                total=int(len(test_set))
+                if test_loader.batch_size > 1:
-                for i in tqdm(range(total), desc=f"Processing {test_set_name}", ncols=100):
+                    Log.error("Batch size should be 1 for inference, found {} in {}".format(test_loader.batch_size, test_set_name), terminate=True)
-                    try:
+
-                        data = test_set.__getitem__(i)
+                total=int(len(test_loader))
-                        scene_name = data["scene_name"]
+                loop = tqdm(enumerate(test_loader), total=total)
-                        inference_result_path = os.path.join(self.output_dir, test_set_name, f"{scene_name}.pkl")
+                for i, data in loop:
-                        
+                    status_manager.set_progress("inference", "inferencer", f"Batch[{test_set_name}]", i+1, total)
-                        if os.path.exists(inference_result_path):
+                    test_set.process_batch(data, self.device)
-                            Log.info(f"Inference result already exists for scene: {scene_name}")
+                    output = self.predict_sequence(data)
-                            continue
+                    self.save_inference_result(test_set_name, data["scene_name"][0], output)
-                        
-                        status_manager.set_progress("inference", "inferencer", f"Batch[{test_set_name}]", i+1, total)
-                        output = self.predict_sequence(data)
-                        self.save_inference_result(test_set_name, data["scene_name"], output)
-                    except Exception as e:
-                        Log.error(f"Error in scene {scene_name}, {e}")
-                        continue
                     
             status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
         
-    def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 10, max_success=3):
+    def predict_sequence(self, data, cr_increase_threshold=0, max_iter=50, max_retry=5):
-        scene_name = data["scene_name"]
+        scene_name = data["scene_name"][0]
         Log.info(f"Processing scene: {scene_name}")
         status_manager.set_status("inference", "inferencer", "scene", scene_name)
 
         ''' data for rendering '''
-        scene_path = data["scene_path"]
+        scene_path = data["scene_path"][0]
-        O_to_L_pose = data["O_to_L_pose"]
+        O_to_L_pose = data["O_to_L_pose"][0]
-        voxel_threshold = self.voxel_size
+        voxel_threshold = data["voxel_threshold"][0]
-        filter_degree = 75
+        filter_degree = data["filter_degree"][0]
-        down_sampled_model_pts = data["gt_pts"]
+        model_points_normals = data["model_points_normals"][0]
-        
+        model_pts = model_points_normals[:,:3]
-        first_frame_to_world_9d = data["first_scanned_n_to_world_pose_9d"][0]
+        down_sampled_model_pts = PtsUtil.voxel_downsample_point_cloud(model_pts, voxel_threshold)
-        first_frame_to_world = np.eye(4)
+        first_frame_to_world_9d = data["first_to_world_9d"][0]
-        first_frame_to_world[:3,:3] = PoseUtil.rotation_6d_to_matrix_numpy(first_frame_to_world_9d[:6])
+        first_frame_to_world = torch.eye(4, device=first_frame_to_world_9d.device)
-        first_frame_to_world[:3,3] = first_frame_to_world_9d[6:]
+        first_frame_to_world[:3,:3] = PoseUtil.rotation_6d_to_matrix_tensor_batch(first_frame_to_world_9d[:,:6])[0]
+        first_frame_to_world[:3,3] = first_frame_to_world_9d[0,6:]
+        first_frame_to_world = first_frame_to_world.to(self.device)
         
         ''' data for inference '''
         input_data = {}
-        input_data["combined_scanned_pts"] = torch.tensor(data["first_scanned_pts"][0], dtype=torch.float32).to(self.device).unsqueeze(0)
+        input_data["scanned_pts"] = [data["first_pts"][0].to(self.device)]
-        input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(data["first_scanned_n_to_world_pose_9d"], dtype=torch.float32).to(self.device)]
+        input_data["scanned_n_to_world_pose_9d"] = [data["first_to_world_9d"][0].to(self.device)]
         input_data["mode"] = namespace.Mode.TEST
-        input_pts_N = input_data["combined_scanned_pts"].shape[1]
+        input_data["combined_scanned_pts"] = data["combined_scanned_pts"]
+        input_pts_N = input_data["scanned_pts"][0].shape[1]
         
-        root = os.path.dirname(scene_path)
+        first_frame_target_pts, _ = RenderUtil.render_pts(first_frame_to_world, scene_path, self.script_path, model_points_normals, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
-        display_table_info = DataLoadUtil.get_display_table_info(root, scene_name)
-        radius = display_table_info["radius"]
-        scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0,display_table_radius=radius))
-
-        first_frame_target_pts, first_frame_target_normals, first_frame_scan_points_indices = RenderUtil.render_pts(first_frame_to_world, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
         scanned_view_pts = [first_frame_target_pts]
-        history_indices = [first_frame_scan_points_indices]
+        last_pred_cr = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
-        last_pred_cr, added_pts_num = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
+
         retry_duplication_pose = []
         retry_no_pts_pose = []
-        retry_overlap_pose = []
         retry = 0
         pred_cr_seq = [last_pred_cr]
-        success = 0
+        while len(pred_cr_seq) < max_iter and retry < max_retry:
-        last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], voxel_threshold).shape[0]
+            
-        import time
-        while len(pred_cr_seq) < max_iter and retry < max_retry and success < max_success:
-            Log.green(f"iter: {len(pred_cr_seq)}, retry: {retry}/{max_retry}, success: {success}/{max_success}")
-            combined_scanned_pts = np.vstack(scanned_view_pts)
-            voxel_downsampled_combined_scanned_pts_np, inverse =  self.voxel_downsample_with_mapping(combined_scanned_pts, voxel_threshold)
             output = self.pipeline(input_data)
             pred_pose_9d = output["pred_pose_9d"]
             pred_pose = torch.eye(4, device=pred_pose_9d.device)
-            # # save pred_pose_9d ------
-            # root = "/media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction/temp_output_result"
-            # scene_dir = os.path.join(root, scene_name)
-            # if not os.path.exists(scene_dir):
-            #     os.makedirs(scene_dir)
-            # pred_9d_path = os.path.join(scene_dir,f"pred_pose_9d_{len(pred_cr_seq)}.npy")
-            # pts_path = os.path.join(scene_dir,f"combined_scanned_pts_{len(pred_cr_seq)}.txt")
-            # np_combined_scanned_pts = input_data["combined_scanned_pts"][0].cpu().numpy()
-            # np.save(pred_9d_path, pred_pose_9d.cpu().numpy())
-            # np.savetxt(pts_path, np_combined_scanned_pts)
-            # # ----- ----- -----
-            predict_result = PredictResult(pred_pose_9d.cpu().numpy(), input_pts=input_data["combined_scanned_pts"][0].cpu().numpy(), cluster_params=dict(eps=0.25, min_samples=3))
-            # -----------------------
-            # import ipdb; ipdb.set_trace()
-            # predict_result.visualize()
-            # -----------------------
-            pred_pose_9d_candidates = predict_result.candidate_9d_poses
-            for pred_pose_9d in pred_pose_9d_candidates:
-                #import ipdb; ipdb.set_trace()
-                pred_pose_9d = torch.tensor(pred_pose_9d, dtype=torch.float32).to(self.device).unsqueeze(0)
-                pred_pose[:3,:3] = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_pose_9d[:,:6])[0]
-                pred_pose[:3,3] = pred_pose_9d[0,6:]
-                try:
-                    new_target_pts, new_target_normals, new_scan_points_indices = RenderUtil.render_pts(pred_pose, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
-                    #import ipdb; ipdb.set_trace()
-                    if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold):
-                        curr_overlap_area_threshold = overlap_area_threshold
-                    else:
-                        curr_overlap_area_threshold = overlap_area_threshold * 0.5  
 
-                    downsampled_new_target_pts = PtsUtil.voxel_downsample_point_cloud(new_target_pts, voxel_threshold)
+            pred_pose[:3,:3] = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_pose_9d[:,:6])[0]
-                    overlap, _ = ReconstructionUtil.check_overlap(downsampled_new_target_pts, voxel_downsampled_combined_scanned_pts_np, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=voxel_threshold, require_new_added_pts_num = True)
+            pred_pose[:3,3] = pred_pose_9d[0,6:]
-                    if not overlap:
-                        Log.yellow("no overlap!")
-                        retry += 1
-                        retry_overlap_pose.append(pred_pose.cpu().numpy().tolist())
-                        continue
-                    
-                    history_indices.append(new_scan_points_indices)
-                except Exception as e:
-                    Log.error(f"Error in scene {scene_path}, {e}")
-                    print("current pose: ", pred_pose)
-                    print("curr_pred_cr: ", last_pred_cr)
-                    retry_no_pts_pose.append(pred_pose.cpu().numpy().tolist())
-                    retry += 1
-                    continue
-                
-                if new_target_pts.shape[0] == 0:
-                    Log.red("no pts in new target")
-                    retry_no_pts_pose.append(pred_pose.cpu().numpy().tolist())
-                    retry += 1
-                    continue
-                
-                pred_cr, _ = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold)
-                Log.yellow(f"{pred_cr}, {last_pred_cr}, max: , {data['seq_max_coverage_rate']}")
-                if pred_cr >= data["seq_max_coverage_rate"] - 1e-3:
-                    print("max coverage rate reached!: ", pred_cr)
-                    
             
-
+            try:
-                pred_cr_seq.append(pred_cr)
+                new_target_pts_world, new_pts_world = RenderUtil.render_pts(pred_pose, scene_path, self.script_path, model_points_normals, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose, require_full_scene=True)
-                scanned_view_pts.append(new_target_pts)
+            except Exception as e:
-                
+                Log.warning(f"Error in scene {scene_path}, {e}")
-                input_data["scanned_n_to_world_pose_9d"] = [torch.cat([input_data["scanned_n_to_world_pose_9d"][0], pred_pose_9d], dim=0)]
+                print("current pose: ", pred_pose)
-                
+                print("curr_pred_cr: ", last_pred_cr)
-                combined_scanned_pts = np.vstack(scanned_view_pts)
+                retry_no_pts_pose.append(pred_pose.cpu().numpy().tolist())
-                voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_pts, voxel_threshold)
+                retry += 1
-                random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N)
+                continue
-                input_data["combined_scanned_pts"] = torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)
-
-                
-                last_pred_cr = pred_cr
-                pts_num = voxel_downsampled_combined_scanned_pts_np.shape[0]
-                Log.info(f"delta pts num:,{pts_num - last_pts_num },{pts_num}, {last_pts_num}")
-
-                if pts_num - last_pts_num < self.min_new_pts_num and pred_cr <= data["seq_max_coverage_rate"] - 1e-2:
-                    retry += 1
-                    retry_duplication_pose.append(pred_pose.cpu().numpy().tolist())
-                    Log.red(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
-                elif pts_num - last_pts_num < self.min_new_pts_num and pred_cr > data["seq_max_coverage_rate"] - 1e-2:
-                    success += 1
-                    Log.success(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
-
-                last_pts_num = pts_num
-                break
             
+            
+            pred_cr = self.compute_coverage_rate(scanned_view_pts, new_target_pts_world, down_sampled_model_pts, threshold=voxel_threshold)
+            
+            print(pred_cr, last_pred_cr, " max: ", data["max_coverage_rate"])
+            if pred_cr >= data["max_coverage_rate"]:
+                print("max coverage rate reached!")
+            if pred_cr <= last_pred_cr + cr_increase_threshold:
+                retry += 1
+                retry_duplication_pose.append(pred_pose.cpu().numpy().tolist())
+                continue
 
+            retry = 0
+            pred_cr_seq.append(pred_cr)
+            scanned_view_pts.append(new_target_pts_world)
+            down_sampled_new_pts_world = PtsUtil.random_downsample_point_cloud(new_pts_world, input_pts_N)
+            new_pts_world_aug = np.hstack([down_sampled_new_pts_world, np.ones((down_sampled_new_pts_world.shape[0], 1))])
+            new_pts = np.dot(np.linalg.inv(first_frame_to_world.cpu()), new_pts_world_aug.T).T[:,:3]
+
+            new_pts_tensor = torch.tensor(new_pts, dtype=torch.float32).unsqueeze(0).to(self.device)
+            
+            input_data["scanned_pts"] = [torch.cat([input_data["scanned_pts"][0] , new_pts_tensor], dim=0)]
+            input_data["scanned_n_to_world_pose_9d"] = [torch.cat([input_data["scanned_n_to_world_pose_9d"][0], pred_pose_9d], dim=0)]
+            combined_scanned_views_pts = np.concatenate(input_data["scanned_pts"][0].tolist(), axis=0)
+            voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_views_pts, 0.002)
+            random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N)
+            input_data["combined_scanned_pts"] = torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)
+
+            
+            last_pred_cr = pred_cr
+
+        
+        input_data["scanned_pts"] = input_data["scanned_pts"][0].cpu().numpy().tolist()
         input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()
         result = {
             "pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"],
-            "combined_scanned_pts": input_data["combined_scanned_pts"],
+            "pts_seq": input_data["scanned_pts"],
             "target_pts_seq": scanned_view_pts,
             "coverage_rate_seq": pred_cr_seq,
-            "max_coverage_rate": data["seq_max_coverage_rate"],
+            "max_coverage_rate": data["max_coverage_rate"][0],
             "pred_max_coverage_rate": max(pred_cr_seq),
             "scene_name": scene_name,
             "retry_no_pts_pose": retry_no_pts_pose,
             "retry_duplication_pose": retry_duplication_pose,
-            "retry_overlap_pose": retry_overlap_pose,
+            "best_seq_len": data["best_seq_len"][0],
-            "best_seq_len": data["best_seq_len"],
         }
         self.stat_result[scene_name] = {
+            "max_coverage_rate": data["max_coverage_rate"][0],
+            "success_rate": max(pred_cr_seq)/ data["max_coverage_rate"][0],
             "coverage_rate_seq": pred_cr_seq,
             "pred_max_coverage_rate": max(pred_cr_seq),
             "pred_seq_len": len(pred_cr_seq),
         }
-        print('success rate: ', max(pred_cr_seq))
+        print('success rate: ', max(pred_cr_seq) / data["max_coverage_rate"][0])
 
         return result
 
@@ -263,14 +198,7 @@ class Inferencer(Runner):
         combined_point_cloud = np.vstack(new_scanned_view_pts)
         down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
         return ReconstructionUtil.compute_coverage_rate(model_pts, down_sampled_combined_point_cloud, threshold)
-    
+        
-    def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
-        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
-        unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
-        idx_sort = np.argsort(inverse)
-        idx_unique = idx_sort[np.cumsum(counts)-counts]
-        downsampled_points = point_cloud[idx_unique]
-        return downsampled_points, inverse
 
     def save_inference_result(self, dataset_name, scene_name, output):
         dataset_dir = os.path.join(self.output_dir, dataset_name)
@@ -278,7 +206,7 @@ class Inferencer(Runner):
             os.makedirs(dataset_dir)
         output_path = os.path.join(dataset_dir, f"{scene_name}.pkl")
         pickle.dump(output, open(output_path, "wb"))
-        with open(self.stat_result_path, "w") as f:
+        with open(os.path.join(dataset_dir, "stat.json"), "w") as f:
             json.dump(self.stat_result, f)
     
 

utils/data_load.py

@@ -24,6 +24,8 @@ class DataLoadUtil:
         for channel in float_channels:
             channel_data = exr_file.channel(channel)
             img_data.append(np.frombuffer(channel_data, dtype=np.float16).reshape((height, width)))
+
+        # 将各通道组合成一个 (height, width, 3) 的 RGB 图像
         img = np.stack(img_data, axis=-1)
         return img
 

utils/pts.py

@@ -14,27 +14,38 @@ class PtsUtil:
             downsampled_points = point_cloud[idx_unique]
             return downsampled_points, idx_unique
         else:
-            unique_voxels = np.unique(voxel_indices, axis=0, return_inverse=True)
+            import ipdb; ipdb.set_trace()
-            return unique_voxels[0]*voxel_size
+            unique_voxels = np.unique(voxel_indices, axis=0, return_inverse=False)
-        
+            return unique_voxels*voxel_size
+
     @staticmethod
-    def voxel_downsample_point_cloud_random(point_cloud, voxel_size=0.005, require_idx=False):
+    def voxel_downsample_point_cloud_o3d(point_cloud, voxel_size=0.005):
-        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
+        pcd = o3d.geometry.PointCloud()
-        unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
+        pcd.points = o3d.utility.Vector3dVector(point_cloud)
-        idx_sort = np.argsort(inverse)
+        pcd = pcd.voxel_down_sample(voxel_size)
-        idx_unique = idx_sort[np.cumsum(counts)-counts]
+        return np.asarray(pcd.points)
-        downsampled_points = point_cloud[idx_unique]
-        if require_idx:
-            return downsampled_points, inverse
-        return downsampled_points
     
     @staticmethod
-    def random_downsample_point_cloud(point_cloud, num_points, require_idx=False):
+    def voxel_downsample_point_cloud_and_trace_o3d(point_cloud, voxel_size=0.005):
+        pcd = o3d.geometry.PointCloud()
+        pcd.points = o3d.utility.Vector3dVector(point_cloud)
+        max_bound = pcd.get_max_bound()
+        min_bound = pcd.get_min_bound()
+        pcd = pcd.voxel_down_sample_and_trace(voxel_size, max_bound, min_bound, True)
+        
+        return np.asarray(pcd.points)
+       
+    @staticmethod
+    def random_downsample_point_cloud(point_cloud, num_points, require_idx=False, replace=True):
         if point_cloud.shape[0] == 0:
             if require_idx:
                 return point_cloud, np.array([])
             return point_cloud
-        idx = np.random.choice(len(point_cloud), num_points, replace=True)
+        if not replace and num_points > len(point_cloud):
+            if require_idx:
+                return point_cloud, np.arange(len(point_cloud))
+            return point_cloud
+        idx = np.random.choice(len(point_cloud), num_points, replace=replace)
         if require_idx:
             return point_cloud[idx], idx
         return point_cloud[idx]

utils/reconstruction.py

@@ -32,15 +32,13 @@ class ReconstructionUtil:
         
     
     @staticmethod
-    def check_overlap(new_point_cloud, combined_point_cloud, overlap_area_threshold=25, voxel_size=0.01, require_new_added_pts_num=False):
+    def check_overlap(new_point_cloud, combined_point_cloud, overlap_area_threshold=25, voxel_size=0.01):
         kdtree = cKDTree(combined_point_cloud)
         distances, _ = kdtree.query(new_point_cloud)
-        overlapping_points_num = np.sum(distances < voxel_size*2)
+        overlapping_points = np.sum(distances < voxel_size*2)
         cm = 0.01
         voxel_size_cm = voxel_size / cm
-        overlap_area = overlapping_points_num * voxel_size_cm * voxel_size_cm
+        overlap_area = overlapping_points * voxel_size_cm * voxel_size_cm
-        if require_new_added_pts_num:
-            return overlap_area > overlap_area_threshold, len(new_point_cloud)-np.sum(distances < voxel_size*1.2)
         return overlap_area > overlap_area_threshold
 
 

utils/render.py

@@ -1,75 +1,16 @@
 
 import os
 import json
-import time
 import subprocess
 import tempfile
 import shutil
-import numpy as np
 from utils.data_load import DataLoadUtil
 from utils.reconstruction import ReconstructionUtil
 from utils.pts import PtsUtil
 class RenderUtil:
-    target_mask_label = (0, 255, 0)
-    display_table_mask_label = (0, 0, 255)
-    random_downsample_N = 32768
-    min_z = 0.2
-    max_z = 0.5
 
     @staticmethod
-    def get_world_points_and_normal(depth, mask, normal, cam_intrinsic, cam_extrinsic, random_downsample_N):
+    def render_pts(cam_pose, scene_path, script_path, model_points_normals, voxel_threshold=0.005, filter_degree=75, nO_to_nL_pose=None, require_full_scene=False):
-        z = depth[mask]
-        i, j = np.nonzero(mask)
-        x = (j - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0]
-        y = (i - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1]
-
-        points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3)
-        normal_camera = normal[mask].reshape(-1, 3)
-        sampled_target_points, idx = PtsUtil.random_downsample_point_cloud(
-                    points_camera, random_downsample_N, require_idx=True
-                )
-        if len(sampled_target_points) == 0:
-            return np.zeros((0, 3)), np.zeros((0, 3))
-        sampled_normal_camera  = normal_camera[idx]
-
-        points_camera_aug = np.concatenate((sampled_target_points, np.ones((sampled_target_points.shape[0], 1))), axis=-1)
-        points_camera_world = np.dot(cam_extrinsic, points_camera_aug.T).T[:, :3]
-        
-        return points_camera_world, sampled_normal_camera
-    
-    @staticmethod
-    def get_world_points(depth, mask, cam_intrinsic, cam_extrinsic, random_downsample_N):
-        z = depth[mask]
-        i, j = np.nonzero(mask)
-        x = (j - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0]
-        y = (i - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1]
-
-        points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3)
-        sampled_target_points = PtsUtil.random_downsample_point_cloud(
-                    points_camera, random_downsample_N
-                )
-        points_camera_aug = np.concatenate((sampled_target_points, np.ones((sampled_target_points.shape[0], 1))), axis=-1)
-        points_camera_world = np.dot(cam_extrinsic, points_camera_aug.T).T[:, :3]
-        
-        return points_camera_world
-    
-    @staticmethod
-    def get_scan_points_indices(scan_points, mask, display_table_mask_label, cam_intrinsic, cam_extrinsic):
-        scan_points_homogeneous = np.hstack((scan_points, np.ones((scan_points.shape[0], 1))))
-        points_camera = np.dot(np.linalg.inv(cam_extrinsic), scan_points_homogeneous.T).T[:, :3]
-        points_image_homogeneous = np.dot(cam_intrinsic, points_camera.T).T
-        points_image_homogeneous /= points_image_homogeneous[:, 2:]
-        pixel_x = points_image_homogeneous[:, 0].astype(int)
-        pixel_y = points_image_homogeneous[:, 1].astype(int)
-        h, w = mask.shape[:2]
-        valid_indices = (pixel_x >= 0) & (pixel_x < w) & (pixel_y >= 0) & (pixel_y < h)
-        mask_colors = mask[pixel_y[valid_indices], pixel_x[valid_indices]]
-        selected_points_indices = np.where((mask_colors == display_table_mask_label).all(axis=-1))[0]
-        selected_points_indices = np.where(valid_indices)[0][selected_points_indices]
-        return selected_points_indices
-
-    @staticmethod
-    def render_pts(cam_pose, scene_path, script_path, scan_points, voxel_threshold=0.005, filter_degree=75, nO_to_nL_pose=None, require_full_scene=False):
         
         nO_to_world_pose = DataLoadUtil.get_real_cam_O_from_cam_L(cam_pose, nO_to_nL_pose, scene_path=scene_path)
 
@@ -83,54 +24,29 @@ class RenderUtil:
             shutil.copy(scene_info_path, os.path.join(temp_dir, "scene_info.json"))
             params_data_path = os.path.join(temp_dir, "params.json")
             with open(params_data_path, 'w') as f:
-                json.dump(params, f) 
+                json.dump(params, f)
             result = subprocess.run([
-                '/home/hofee/blender-4.0.2-linux-x64/blender', '-b', '-P', script_path, '--', temp_dir
+                'blender', '-b', '-P', script_path, '--', temp_dir
             ], capture_output=True, text=True)
-            #print(result)
+            if result.returncode != 0:
-            #import ipdb; ipdb.set_trace()
+                print("Blender script failed:")
+                print(result.stderr)
+                return None
             path = os.path.join(temp_dir, "tmp")
-            cam_info = DataLoadUtil.load_cam_info(path, binocular=True)
+            point_cloud = DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True)
-            depth_L, depth_R = DataLoadUtil.load_depth(
+            cam_params = DataLoadUtil.load_cam_info(path, binocular=True)
-                    path, cam_info["near_plane"], 
+            
-                    cam_info["far_plane"], 
+            ''' TODO: old code: filter_points api is changed, need to update the code '''
-                    binocular=True
+            filtered_point_cloud = PtsUtil.filter_points(point_cloud, model_points_normals, cam_pose=cam_params["cam_to_world"], voxel_size=voxel_threshold, theta=filter_degree)
-                )
+            full_scene_point_cloud = None
-            mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True)
+            if require_full_scene:
-            normal_L = DataLoadUtil.load_normal(path, binocular=True, left_only=True)
+                depth_L, depth_R = DataLoadUtil.load_depth(path, cam_params['near_plane'], cam_params['far_plane'], binocular=True)
-            ''' target points '''
+                point_cloud_L = DataLoadUtil.get_point_cloud(depth_L, cam_params['cam_intrinsic'], cam_params['cam_to_world'])['points_world']
-            mask_img_L = mask_L
+                point_cloud_R = DataLoadUtil.get_point_cloud(depth_R, cam_params['cam_intrinsic'], cam_params['cam_to_world_R'])['points_world']
-            mask_img_R = mask_R
+            
-
+                point_cloud_L = PtsUtil.random_downsample_point_cloud(point_cloud_L, 65536)
-            target_mask_img_L = (mask_L == RenderUtil.target_mask_label).all(axis=-1)
+                point_cloud_R = PtsUtil.random_downsample_point_cloud(point_cloud_R, 65536)
-            target_mask_img_R = (mask_R == RenderUtil.target_mask_label).all(axis=-1)
+                full_scene_point_cloud = PtsUtil.get_overlapping_points(point_cloud_L, point_cloud_R)
 
             
-            sampled_target_points_L, sampled_target_normal_L = RenderUtil.get_world_points_and_normal(depth_L,target_mask_img_L,normal_L, cam_info["cam_intrinsic"], cam_info["cam_to_world"],  RenderUtil.random_downsample_N)
+            return filtered_point_cloud, full_scene_point_cloud
-            sampled_target_points_R = RenderUtil.get_world_points(depth_R, target_mask_img_R, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"],  RenderUtil.random_downsample_N  )
-
-
-            has_points = sampled_target_points_L.shape[0] > 0 and sampled_target_points_R.shape[0] > 0
-            if has_points:
-                target_points, overlap_idx = PtsUtil.get_overlapping_points(
-                        sampled_target_points_L, sampled_target_points_R, voxel_threshold, require_idx=True
-                    )
-                sampled_target_normal_L = sampled_target_normal_L[overlap_idx]
-
-            if has_points:
-                has_points = target_points.shape[0] > 0
-
-            if has_points:
-                target_points, target_normals = PtsUtil.filter_points(
-                    target_points, sampled_target_normal_L, cam_info["cam_to_world"], theta_limit = filter_degree, z_range=(RenderUtil.min_z, RenderUtil.max_z)
-                    )
-                
-            
-            scan_points_indices_L = RenderUtil.get_scan_points_indices(scan_points, mask_img_L, RenderUtil.display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world"]) 
-            scan_points_indices_R = RenderUtil.get_scan_points_indices(scan_points, mask_img_R, RenderUtil.display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"])
-            scan_points_indices = np.intersect1d(scan_points_indices_L, scan_points_indices_R)
-            if not has_points:
-                target_points = np.zeros((0, 3))
-                target_normals = np.zeros((0, 3))
-            #import ipdb; ipdb.set_trace()
-            return target_points, target_normals, scan_points_indices

utils/vis.py

@@ -7,7 +7,6 @@ import trimesh
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from utils.data_load import DataLoadUtil
 from utils.pts import PtsUtil
-from utils.pose import PoseUtil
 
 class visualizeUtil:
     
@@ -34,22 +33,7 @@ class visualizeUtil:
         all_cam_axis = np.array(all_cam_axis).reshape(-1, 3)
         np.savetxt(os.path.join(output_dir, "all_cam_pos.txt"), all_cam_pos)
         np.savetxt(os.path.join(output_dir, "all_cam_axis.txt"), all_cam_axis)
-    
+        
-    @staticmethod
-    def get_cam_pose_and_cam_axis(cam_pose, is_6d_pose):
-        if is_6d_pose:
-            matrix_cam_pose = np.eye(4)
-            matrix_cam_pose[:3,:3] = PoseUtil.rotation_6d_to_matrix_numpy(cam_pose[:6])
-            matrix_cam_pose[:3, 3] = cam_pose[6:]
-        else:
-            matrix_cam_pose = cam_pose
-        cam_pos = matrix_cam_pose[:3, 3]
-        cam_axis = matrix_cam_pose[:3, 2]
-        num_samples = 10
-        sample_points = [cam_pos + 0.02*t * cam_axis for t in range(num_samples)]
-        sample_points = np.array(sample_points)
-        return cam_pos, sample_points
-    
     @staticmethod
     def save_all_combined_pts(root, scene, output_dir):
         length = DataLoadUtil.get_scene_seq_length(root, scene)