upd

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: overfit_ab_global_and_local
+    name: overfit_ab_local_only
     root_dir: "experiments"
     use_checkpoint: False
     epoch: -1 # -1 stands for last epoch
@@ -106,7 +106,7 @@ module:
   gf_view_finder:
     t_feat_dim: 128
     pose_feat_dim: 256
-    main_feat_dim: 2560
+    main_feat_dim: 2048
     regression_head: Rx_Ry_and_T
     pose_mode: rot_matrix
     per_point_feature: False

core/nbv_dataset.py

@@ -165,13 +165,8 @@ class NBVReconstructionDataset(BaseDataset):
             [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)
-        
         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_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)
@@ -203,9 +198,6 @@ class NBVReconstructionDataset(BaseDataset):
             collate_data["best_to_world_pose_9d"] = torch.stack(
                 [torch.tensor(item["best_to_world_pose_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 [
@@ -213,7 +205,6 @@ class NBVReconstructionDataset(BaseDataset):
                     "scanned_pts_mask",
                     "scanned_n_to_world_pose_9d",
                     "best_to_world_pose_9d",
-                    "combined_scanned_pts",
                 ]:
                     collate_data[key] = [item[key] for item in batch]
             return collate_data

core/pipeline.py

@@ -99,11 +99,6 @@ class NBVReconstructionPipeline(nn.Module):
 
         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(
-            combined_scanned_pts_batch, require_per_point_feat=False
-        )  # global_scanned_feat: Tensor(B x Dg)
-
         for scanned_n_to_world_pose_9d, scanned_pts in zip(scanned_n_to_world_pose_9d_batch, scanned_pts_batch):
             scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)  # Tensor(S x 9)
             scanned_pts = scanned_pts.to(device)  # Tensor(S x N x 3)
@@ -113,7 +108,7 @@ class NBVReconstructionPipeline(nn.Module):
             embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp+Dl))
         
         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))
+        main_feat = seq_feat # Tensor(B x Ds)
 
         if torch.isnan(main_feat).any():
             Log.error("nan in main_feat", True)