update

utils/pts_util.py

@@ -117,66 +117,6 @@ class PtsUtil:
         filtered_sampled_points = sampled_points[idx]
         return filtered_sampled_points[:, :3]
 
-    @staticmethod
-    def old_register(pcl: np.ndarray, model: trimesh.Trimesh, voxel_size=0.002):
-        radius_normal = voxel_size * 3
-        pipreg = o3d.pipelines.registration
-        model_pcd = o3d.geometry.PointCloud()
-        model_pcd.points = o3d.utility.Vector3dVector(model.vertices)
-        model_downsampled = model_pcd.voxel_down_sample(voxel_size)
-        model_downsampled.estimate_normals(
-            search_param=o3d.geometry.KDTreeSearchParamHybrid(
-                radius=radius_normal, max_nn=30
-            )
-        )
-        model_fpfh = pipreg.compute_fpfh_feature(
-            model_downsampled,
-            o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=100),
-        )
-
-        source_pcd = o3d.geometry.PointCloud()
-        source_pcd.points = o3d.utility.Vector3dVector(pcl)
-        source_downsampled = source_pcd.voxel_down_sample(voxel_size)
-        source_downsampled.estimate_normals(
-            search_param=o3d.geometry.KDTreeSearchParamHybrid(
-                radius=radius_normal, max_nn=30
-            )
-        )
-        source_fpfh = pipreg.compute_fpfh_feature(
-            source_downsampled,
-            o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=100),
-        )
-
-        reg_ransac = pipreg.registration_ransac_based_on_feature_matching(
-            source_downsampled,
-            model_downsampled,
-            source_fpfh,
-            model_fpfh,
-            mutual_filter=True,
-            max_correspondence_distance=voxel_size * 2,
-            estimation_method=pipreg.TransformationEstimationPointToPoint(False),
-            ransac_n=4,
-            checkers=[pipreg.CorrespondenceCheckerBasedOnEdgeLength(0.9)],
-            criteria=pipreg.RANSACConvergenceCriteria(4000000, 500),
-        )
-        
-        reg_icp = pipreg.registration_icp(
-            source_downsampled,
-            model_downsampled,
-            voxel_size/2,
-            reg_ransac.transformation,
-            pipreg.TransformationEstimationPointToPlane(),
-            pipreg.ICPConvergenceCriteria(max_iteration=2000),
-        )
-        
-        return reg_icp.transformation
-    
-    @staticmethod
-    def chamfer_distance(pcl_a, pcl_b):
-        distances = np.linalg.norm(pcl_a[:, None] - pcl_b, axis=2)
-        min_distances = np.min(distances, axis=1)
-        return np.sum(min_distances)
-    
     @staticmethod
     def multi_scale_icp(
         source, target, voxel_size_range, init_transformation=None, steps=20