请你写出点云超体聚类+LCCP程序

点云超体聚类+LCCP程序是一种将点云分割成多个超体素，并在每个超体素内进行聚类的方法，并使用LCCP算法进行分割的程序。以下是一份基于Python和open3d库实现的点云超体聚类+LCCP程序的代码示例：

import open3d as o3d

# 读取点云
pcd = o3d.io.read_point_cloud("point_cloud.pcd")

# 超体素聚类
voxel_size = 0.05 # 超体素大小
pcd = pcd.voxel_down_sample(voxel_size) # 下采样
labels = pcd.cluster_dbscan(eps=0.05, min_points=10) # DBSCAN聚类
max_label = labels.max()
print(f"point cloud has {max_label + 1} clusters")

# LCCP分割
segmentation = pcd.segment_plane(distance_threshold=0.01, ransac_n=3, num_iterations=1000)
segment_labels = np.asarray(segmentation[1])
pcd_normals = pcd.compute_point_cloud_normals(knn=knn)
pcd_labels = np.array(labels)
pcd_segments = o3d.geometry.PointCloud()
for segment_id in np.unique(segment_labels):
    segment_mask = segment_labels == segment_id
    segment_pcd = pcd.select_by_index(np.where(segment_mask)[0])
    segment_normals = pcd_normals.select_by_index(np.where(segment_mask)[0])
    segment_labels = np.array(pcd_labels[np.where(segment_mask)[0]])
    lccp = segment_pcd.segment_local_maxima(
        search_radius=0.2, suppress_radius=0.1, 
        normal_radius=0.1, method=o3d.geometry.SegmentLocalMaximaMethod.LCCP)
    segments = lccp.get_segmented_point_cloud()
    segments.normals = segment_normals
    segments.colors = np.array([segment_id / len(np.unique(segment_labels)) for i in range(len(segments.points))])
    pcd_segments += segments

# 可视化结果
o3d.visualization.draw_geometries([pcd_segments])

其中，`read_point_cloud()`函数用于读取点云数据，`voxel_down_sample()`函数用于进行超体素分割的下采样操作，`cluster_dbscan()`函数用于进行DBSCAN聚类，`segment_plane()`函数用于平面分割，`compute_point_cloud_normals()`函数用于计算点云法向量，`select_by_index()`函数用于选取指定索引的点，`segment_local_maxima()`函数用于进行LCCP分割，`get_segmented_point_cloud()`函数用于获取分割结果，`draw_geometries()`函数用于可视化分割结果。