update scan_points strategy

2024-10-02 16:24:13 +08:00
parent 551282a0ec
commit c8b8a44252
6 changed files with 120 additions and 50 deletions
--- a/utils/data_load.py
+++ b/utils/data_load.py
@@ -322,7 +322,7 @@ class DataLoadUtil:
        random_downsample_N=65536,
        voxel_size=0.005,
        target_mask_label=(0, 255, 0, 255),
-        display_table_mask_label=(255, 0, 0, 255),
+        display_table_mask_label=(0, 0, 255, 255),
        get_display_table_pts=False
    ):
        cam_info = DataLoadUtil.load_cam_info(path, binocular=binocular)
@@ -369,6 +369,12 @@ class DataLoadUtil:
                    mask_R,
                    display_table_mask_label,
                )["points_world"]
+                display_pts_L = PtsUtil.random_downsample_point_cloud(
+                    display_pts_L, random_downsample_N
+                )
+                point_cloud_R = PtsUtil.random_downsample_point_cloud(
+                    display_pts_R, random_downsample_N
+                )
                display_pts_overlap = DataLoadUtil.get_overlapping_points(
                    display_pts_L, display_pts_R, voxel_size
                )
--- a/utils/pts.py
+++ b/utils/pts.py
@@ -19,6 +19,8 @@ class PtsUtil:
    
    @staticmethod
    def random_downsample_point_cloud(point_cloud, num_points):
+        if point_cloud.shape[0] == 0:
+            return point_cloud
        idx = np.random.choice(len(point_cloud), num_points, replace=True)
        return point_cloud[idx]
    
--- a/utils/reconstruction.py
+++ b/utils/reconstruction.py
@@ -8,7 +8,7 @@ class ReconstructionUtil:
    def compute_coverage_rate(target_point_cloud, combined_point_cloud, threshold=0.01):
        kdtree = cKDTree(combined_point_cloud)
        distances, _ = kdtree.query(target_point_cloud)
-        covered_points = np.sum(distances < threshold)
+        covered_points = np.sum(distances < threshold*2)
        coverage_rate = covered_points / target_point_cloud.shape[0]
        return coverage_rate
    
@@ -17,7 +17,10 @@ class ReconstructionUtil:
        kdtree = cKDTree(combined_point_cloud)
        distances, _ = kdtree.query(new_point_cloud)
        overlapping_points = np.sum(distances < threshold)
-        overlap_rate = overlapping_points / new_point_cloud.shape[0]
+        if new_point_cloud.shape[0] == 0:
+            overlap_rate = 0
+        else:
+            overlap_rate = overlapping_points / new_point_cloud.shape[0]
        return overlap_rate
    
    @staticmethod
@@ -43,12 +46,23 @@ class ReconstructionUtil:
                best_view = view_index
        return best_view, best_coverage_increase

+    @staticmethod
+    def get_new_added_points(old_combined_pts, new_pts, threshold=0.005):
+        if old_combined_pts.size == 0:
+            return new_pts
+        if new_pts.size == 0:
+            return np.array([])
+
+        tree = cKDTree(old_combined_pts)
+        distances, _ = tree.query(new_pts, k=1)
+        new_added_points = new_pts[distances > threshold]
+        return new_added_points
    
    @staticmethod
-    def compute_next_best_view_sequence_with_overlap(target_point_cloud, point_cloud_list, scan_points_indices_list, threshold=0.01, overlap_threshold=0.3, init_view = 0, status_info=None):
+    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)
-        combined_scan_points_indices = scan_points_indices_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)
        current_coverage = new_coverage
@@ -62,16 +76,23 @@ class ReconstructionUtil:
            best_coverage_increase = -1
        
            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(combined_scan_points_indices, new_scan_points_indices):
-                        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)
-                        if overlap_rate < overlap_threshold:
-                            continue
+                    
+                    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)
+                    if overlap_rate < overlap_threshold:
+                        continue
                    
                candidate_views = selected_views + [point_cloud_list[view_index]]
                combined_point_cloud = np.vstack(candidate_views)
@@ -88,7 +109,7 @@ class ReconstructionUtil:
                    break
                selected_views.append(point_cloud_list[best_view])
                remaining_views.remove(best_view)
-                combined_scan_points_indices = ReconstructionUtil.combine_scan_points_indices(combined_scan_points_indices, scan_points_indices_list[best_view])
+                history_indices.append(scan_points_indices_list[best_view])
                current_coverage += best_coverage_increase
                cnt_processed_view += 1
                if status_info is not None:
@@ -110,7 +131,7 @@ class ReconstructionUtil:
        return view_sequence, remaining_views, down_sampled_combined_point_cloud
    
    @staticmethod
-    def filter_points(points, points_normals, cam_pose,  voxel_size=0.005, theta=45):
+    def filter_points(points, points_normals, cam_pose,  voxel_size=0.005, theta=75):
        sampled_points = PtsUtil.voxel_downsample_point_cloud(points, voxel_size)
        kdtree = cKDTree(points_normals[:,:3])
        _, indices = kdtree.query(sampled_points)
@@ -143,6 +164,7 @@ class ReconstructionUtil:
    
    @staticmethod
    def compute_covered_scan_points(scan_points, point_cloud, threshold=0.01):
+        
        tree = cKDTree(point_cloud)
        covered_points = []
        indices = []
@@ -153,10 +175,10 @@ class ReconstructionUtil:
        return covered_points, indices
    
    @staticmethod
-    def check_scan_points_overlap(indices1, indices2, threshold=5):
-        return len(set(indices1).intersection(set(indices2))) > threshold
-    
-    @staticmethod
-    def combine_scan_points_indices(indices1, indices2):
-        combined_indices = set(indices1) | set(indices2)
-        return sorted(combined_indices)
+    def check_scan_points_overlap(history_indices, indices2, threshold=5):
+        for indices1 in history_indices:
+            if len(set(indices1).intersection(set(indices2))) >= threshold:
+                return True
+        return False
+
+