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add scan points check

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hofee
2024-09-30 00:55:34 +08:00
parent 2f6d156abd
commit cef7ab4429
3 changed files with 52 additions and 19 deletions
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55
utils/reconstruction.py
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@@ -45,9 +45,10 @@ class ReconstructionUtil:
@staticmethod
def compute_next_best_view_sequence_with_overlap(target_point_cloud, point_cloud_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, overlap_threshold=0.3, init_view = 0, 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]
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
@@ -63,12 +64,14 @@ class ReconstructionUtil:
for view_index in remaining_views:
if selected_views:
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
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
candidate_views = selected_views + [point_cloud_list[view_index]]
combined_point_cloud = np.vstack(candidate_views)
@@ -85,6 +88,7 @@ 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])
current_coverage += best_coverage_increase
cnt_processed_view += 1
if status_info is not None:
@@ -120,4 +124,39 @@ class ReconstructionUtil:
filtered_sampled_points= sampled_points[cos_theta > np.cos(theta_rad)]
return filtered_sampled_points[:, :3]
@staticmethod
def generate_scan_points(display_table_top, display_table_radius, min_distance=0.03, max_points_num = 100, max_attempts = 1000):
points = []
attempts = 0
while len(points) < max_points_num and attempts < max_attempts:
angle = np.random.uniform(0, 2 * np.pi)
r = np.random.uniform(0, display_table_radius)
x = r * np.cos(angle)
y = r * np.sin(angle)
z = display_table_top
new_point = (x, y, z)
if all(np.linalg.norm(np.array(new_point) - np.array(existing_point)) >= min_distance for existing_point in points):
points.append(new_point)
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(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)