python - 过滤点云

Question

我希望尽可能高效地过滤加载有 opend3d 的点云。

目前，我先对点进行下采样，然后再用它们制作网格，然后在我手动创建的包含体网格上使用 .contains。像这样:

    def load_pointcloud(self, pointcloud_path): 
        # Load Pointcloud
        print('target_pointcloud', pointcloud_path)
        self.pointcloud_path = pointcloud_path

        pcd = o3d.io.read_point_cloud(pointcloud_path)
        downpcd = pcd.voxel_down_sample(voxel_size=0.02)
        cl, ind = downpcd.remove_statistical_outlier(nb_neighbors=20,
                                                std_ratio=2.0)
        downpcd = downpcd.select_by_index(ind)
        pcd_points = np.asarray(downpcd.points, dtype=np.float32)

        self.verts = torch.from_numpy(pcd_points)
        self.verts = self.verts.to(device)

        # We construct a Meshes structure for the target mesh
        self.pointcloud_points = Pointclouds(points=[self.verts])
        self.points = pcd_points
        self.inclusion_pointcloud()

    def inclusion_pointcloud(self):
        vetices_in_mesh_states = self.mesh_inclusion.contains(self.points)
        vetices_in_mesh = self.points[vetices_in_mesh_states == True]

        # Creating cropped point cloud
        cropped_pc = o3d.geometry.PointCloud()
        cropped_pc.points = o3d.utility.Vector3dVector(vetices_in_mesh)
        cropped_pc.paint_uniform_color([0,0,0])

        self.points = np.asarray(cropped_pc.points, dtype=np.float32)
        self.verts = torch.from_numpy(self.points)
        self.verts = self.verts.to(device)
        self.pointcloud_points = Pointclouds(points=[self.verts])
        self.pc_mesh = trimesh.Trimesh(vertices=self.points)  

我想做的是，在下采样之后，屏蔽掉 X、Y 和 Z 上的点，然后制作一个网格以在同一包含体积中再次使用 .contains。我认为这会减少 .contains 计算并运行得更快，确实如此，但只是边际减少，例如 10 或 15 毫秒，有时甚至更少。像这样:

    def new_load_pointcloud(self, pointcloud_path): 
        # Load Pointcloud
        print('target_pointcloud', pointcloud_path)
        self.pointcloud_path = pointcloud_path

        pcd = self.trim_cloud(pointcloud_path)
        downpcd = pcd.voxel_down_sample(voxel_size=0.02)
        cl, ind = downpcd.remove_statistical_outlier(nb_neighbors=20,
                                                std_ratio=2.0)
        downpcd = downpcd.select_by_index(ind)
        pcd_points = np.asarray(downpcd.points, dtype=np.float32)

        self.verts = torch.from_numpy(pcd_points)
        self.verts = self.verts.to(device)

        # We construct a Meshes structure for the target mesh
        self.pointcloud_points = Pointclouds(points=[self.verts])
        self.points = pcd_points
        self.inclusion_pointcloud()

    def trim_cloud(self, pointcloud_path):
        # pcd = o3d.io.read_point_cloud(pointcloud_path)
        pcd_clean = o3d.io.read_point_cloud(pointcloud_path)

        # X Axis
        points = np.asarray(pcd_clean.points)
        mask_x_1 = points[:,0] > -0.4
        mask_x_2 = points[:,0] < 0.4

        # Y Axis
        mask_y_1 = points[:,1] > -1.3
        mask_y_2 = points[:,1] < 0.9

        # Z Axis
        mask_z_1 = points[:,2] < 0.3 # Closer to floor     
        mask_z_2 = points[:,2] > -0.1 # Clooser to ceiling

        mask_x = np.logical_and(mask_x_1, mask_x_2) # Along table's wide
        mask_y = np.logical_and(mask_y_1, mask_y_2) # Along table's longitude
        mask_z = np.logical_and(mask_z_1, mask_z_2) # Along table's height
        mask = np.logical_and(mask_x, mask_y, mask_z)
        pcd_clean.points = o3d.utility.Vector3dVector(points[mask])

        return pcd_clean

    def inclusion_pointcloud(self):
        vetices_in_mesh_states = self.mesh_inclusion.contains(self.points)
        vetices_in_mesh = self.points[vetices_in_mesh_states == True]

        # Creating cropped point cloud
        cropped_pc = o3d.geometry.PointCloud()
        cropped_pc.points = o3d.utility.Vector3dVector(vetices_in_mesh)
        cropped_pc.paint_uniform_color([0,0,0])

        self.points = np.asarray(cropped_pc.points, dtype=np.float32)
        self.verts = torch.from_numpy(self.points)
        self.verts = self.verts.to(device)
        self.pointcloud_points = Pointclouds(points=[self.verts])
        self.pc_mesh = trimesh.Trimesh(vertices=self.points)  

Answer 1

我认为您在过滤器中使用了过多的 nb_neighbors。尝试更少的分数，例如 6 或 10，以及更好的阈值，例如 1.0 甚至 0.5。这是 MATLAB 文档中的相同过滤器 https://www.mathworks.com/help/vision/ref/pcdenoise.html ，阈值的标准值为 1.0，knn 的标准值为 6。您也可以尝试使用半径离群值去除或中值过滤器:https://www.mathworks.com/help/lidar/ref/pcmedian.html