我希望以最高效的方式过滤使用opend3d加载的点云。
目前，我在将点云制作成网格并在手动创建的包含体网格上使用.contains之前，执行对点云进行降采样处理。类似于这样：
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，在对点云进行下采样之后，将 X、Y 和 Z 轴上的点屏蔽掉，并生成一个网格来再次在同一包含体积中使用 .contains 方法。我认为这会减少.contains 的计算时间，使代码运行更快，但实际效果较小，通常只能节省 10 或 15 毫秒，有时甚至更少。 示例代码如下：
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)