remove o3d voxel_downsample

runners/strategy_generator.py

@@ -73,7 +73,7 @@ class StrategyGenerator(Runner):
     def generate_sequence(self, root, scene_name, voxel_threshold, soft_overlap_threshold, hard_overlap_threshold):
         status_manager.set_status("generate_strategy", "strategy_generator", "scene", scene_name)
         frame_num = DataLoadUtil.get_scene_seq_length(root, scene_name)
-        import ipdb; ipdb.set_trace()
+        
         model_points_normals = DataLoadUtil.load_points_normals(root, scene_name)
         model_pts = model_points_normals[:,:3]
         down_sampled_model_pts = PtsUtil.voxel_downsample_point_cloud(model_pts, voxel_threshold)

utils/pts.py

@@ -1,16 +1,14 @@
 import numpy as np
 import open3d as o3d
 import torch
-from scipy.spatial import cKDTree
 
 class PtsUtil:
 
     @staticmethod
     def voxel_downsample_point_cloud(point_cloud, voxel_size=0.005):
-        o3d_pc = o3d.geometry.PointCloud()
-        o3d_pc.points = o3d.utility.Vector3dVector(point_cloud)
-        downsampled_pc = o3d_pc.voxel_down_sample(voxel_size)
-        return np.asarray(downsampled_pc.points)
+        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
+        unique_voxels = np.unique(voxel_indices, axis=0, return_inverse=True)
+        return unique_voxels[0]*voxel_size
     
     @staticmethod
     def random_downsample_point_cloud(point_cloud, num_points, require_idx=False):
@@ -95,4 +93,7 @@ class PtsUtil:
         
         return z_filtered_points[:, :3]
     
+    @staticmethod
+    def point_to_hash(point, voxel_size):
+        return tuple(np.floor(point / voxel_size).astype(int))
     

utils/reconstruction.py

@@ -12,6 +12,18 @@ class ReconstructionUtil:
         coverage_rate = covered_points_num / target_point_cloud.shape[0]
         return coverage_rate, covered_points_num
     
+    def compute_coverage_rate_with_normal(target_point_cloud, combined_point_cloud, target_normal, combined_normal, threshold=0.01, normal_threshold=0.1):
+        kdtree = cKDTree(combined_point_cloud)
+        distances, indices = kdtree.query(target_point_cloud)
+        is_covered_by_distance = distances < threshold
+        normal_dots = np.einsum('ij,ij->i', target_normal, combined_normal[indices])
+        is_covered_by_normal = normal_dots > normal_threshold
+        covered_points_num = np.sum(is_covered_by_distance & is_covered_by_normal)
+        coverage_rate = covered_points_num / target_point_cloud.shape[0]
+
+        return coverage_rate, covered_points_num
+        
+    
     @staticmethod
     def compute_overlap_rate(new_point_cloud, combined_point_cloud, threshold=0.01):
         kdtree = cKDTree(combined_point_cloud)
@@ -145,18 +157,6 @@ class ReconstructionUtil:
             attempts += 1
         return points
     
-    @staticmethod
-    def compute_covered_scan_points(scan_points, point_cloud, threshold=0.01):
-        
-        tree = cKDTree(point_cloud)
-        covered_points = []
-        indices = []
-        for i, scan_point in enumerate(scan_points):
-            if tree.query_ball_point(scan_point, threshold):
-                covered_points.append(scan_point)
-                indices.append(i)
-        return covered_points, indices
-    
     @staticmethod
     def check_scan_points_overlap(history_indices, indices2, threshold=5):
         for indices1 in history_indices: