update reconstruction

utils/reconstruction.py

@@ -22,29 +22,7 @@ class ReconstructionUtil:
         else:
             overlap_rate = overlapping_points / new_point_cloud.shape[0]
         return overlap_rate
-    
-    @staticmethod
-    def combine_point_with_view_sequence(point_list, view_sequence):
-        selected_views = []
-        for view_index, _ in view_sequence:
-            selected_views.append(point_list[view_index])
-        return np.vstack(selected_views)
-    
-    @staticmethod 
-    def compute_next_view_coverage_list(views, combined_point_cloud, target_point_cloud, threshold=0.01):
-        best_view = None
-        best_coverage_increase = -1
-        current_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, combined_point_cloud, threshold)
-        
-        for view_index, view in enumerate(views):
-            candidate_views = combined_point_cloud + [view]
-            down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(candidate_views, threshold)
-            new_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, down_sampled_combined_point_cloud, threshold)
-            coverage_increase = new_coverage - current_coverage
-            if coverage_increase > best_coverage_increase:
-                best_coverage_increase = coverage_increase
-                best_view = view_index
-        return best_view, best_coverage_increase
+
 
     @staticmethod
     def get_new_added_points(old_combined_pts, new_pts, threshold=0.005):
@@ -60,54 +38,70 @@ class ReconstructionUtil:
     
     @staticmethod
     def compute_next_best_view_sequence_with_overlap(target_point_cloud, point_cloud_list, scan_points_indices_list, threshold=0.01, soft_overlap_threshold=0.5, hard_overlap_threshold=0.7, init_view = 0, scan_points_threshold=5, status_info=None):
-        selected_views = [point_cloud_list[init_view]]
-        combined_point_cloud = np.vstack(selected_views)
+        selected_views = [init_view]
+        combined_point_cloud = point_cloud_list[init_view]
         history_indices = [scan_points_indices_list[init_view]]
-        down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
-        new_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, down_sampled_combined_point_cloud, threshold)
+        
+        max_rec_pts = np.vstack(point_cloud_list)
+        downsampled_max_rec_pts = PtsUtil.voxel_downsample_point_cloud(max_rec_pts, threshold)
+        
+        max_rec_pts_num = downsampled_max_rec_pts.shape[0]
+        max_rec_pts_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, downsampled_max_rec_pts, threshold)
+        
+        new_coverage = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, combined_point_cloud, threshold)
         current_coverage = new_coverage
         remaining_views = list(range(len(point_cloud_list)))
         view_sequence = [(init_view, current_coverage)]
         cnt_processed_view = 0
         remaining_views.remove(init_view)
-
+        curr_rec_pts_num = combined_point_cloud.shape[0]
+        
+        import time
         while remaining_views:
             best_view = None
             best_coverage_increase = -1
+            best_combined_point_cloud = None
         
             for view_index in remaining_views:
                 if point_cloud_list[view_index].shape[0] == 0:
                     continue
-
                 if selected_views:
                     new_scan_points_indices = scan_points_indices_list[view_index]
-                    
                     if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold):
                         overlap_threshold = hard_overlap_threshold
                     else:
                         overlap_threshold = soft_overlap_threshold
-                        
-                    combined_old_point_cloud = np.vstack(selected_views)
-                    down_sampled_old_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_old_point_cloud,threshold)
-                    down_sampled_new_view_point_cloud = PtsUtil.voxel_downsample_point_cloud(point_cloud_list[view_index],threshold)
-                    overlap_rate = ReconstructionUtil.compute_overlap_rate(down_sampled_new_view_point_cloud,down_sampled_old_point_cloud, threshold)
+                    start = time.time()
+                    overlap_rate = ReconstructionUtil.compute_overlap_rate(point_cloud_list[view_index],combined_point_cloud, threshold)
+                    end = time.time()
+                    # print(f"overlap_rate Time: {end-start}")
                     if overlap_rate < overlap_threshold:
                         continue
-                    
-                candidate_views = selected_views + [point_cloud_list[view_index]]
-                combined_point_cloud = np.vstack(candidate_views)
-                down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
-                new_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, down_sampled_combined_point_cloud, threshold)
+                
+                start = time.time()
+                new_combined_point_cloud = np.vstack([combined_point_cloud, point_cloud_list[view_index]])
+                new_downsampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(new_combined_point_cloud,threshold)
+                new_coverage = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, new_downsampled_combined_point_cloud, threshold)
+                end = time.time()   
+                #print(f"compute_coverage_rate Time: {end-start}")
                 coverage_increase = new_coverage - current_coverage
                 if coverage_increase > best_coverage_increase:
                     best_coverage_increase = coverage_increase
                     best_view = view_index
+                    best_combined_point_cloud = new_downsampled_combined_point_cloud
                     
             
             if best_view is not None:
-                if best_coverage_increase <=3e-3:
+                if best_coverage_increase <=1e-3:
                     break
-                selected_views.append(point_cloud_list[best_view])
+                
+                selected_views.append(best_view)
+                best_rec_pts_num = best_combined_point_cloud.shape[0]
+                print(f"Current rec pts num: {curr_rec_pts_num}, Best rec pts num: {best_rec_pts_num}, Max rec pts num: {max_rec_pts_num}")
+                print(f"Current coverage: {current_coverage}, Best coverage increase: {best_coverage_increase}, Max coverage: {max_rec_pts_coverage}")
+                
+                curr_rec_pts_num = best_rec_pts_num
+                combined_point_cloud = best_combined_point_cloud
                 remaining_views.remove(best_view)
                 history_indices.append(scan_points_indices_list[best_view])
                 current_coverage += best_coverage_increase
@@ -123,12 +117,15 @@ class ReconstructionUtil:
                 
             else:
                 break
+        # ----- Debug Trace ----- #
+        import ipdb; ipdb.set_trace()
+        # ------------------------ #
         if status_info is not None:
             sm = status_info["status_manager"]
             app_name = status_info["app_name"]
             runner_name = status_info["runner_name"]
             sm.set_progress(app_name, runner_name, "processed view", len(point_cloud_list), len(point_cloud_list))
-        return view_sequence, remaining_views, down_sampled_combined_point_cloud
+        return view_sequence, remaining_views, combined_point_cloud
 
     
     @staticmethod