点云主曲率Python 代码

以下是使用Python计算点云主曲率的示例代码：

import numpy as np
from sklearn.neighbors import KDTree

def compute_curvature(points, k=20):
    """
    计算点云的主曲率
    :param points: 点云数组，每行表示一个点的xyz坐标
    :param k: 每个点的邻居数量
    :return: 主曲率数组
    """
    tree = KDTree(points)
    N = points.shape[0]
    curvature = np.zeros((N,))

    for i in range(N):
        idx = tree.query(points[i, :].reshape(1,-1), k=k, return_distance=False)[0]
        neighbor_pts = points[idx, :]
        neighbor_pts -= np.mean(neighbor_pts, axis=0)

        cov = np.cov(neighbor_pts, rowvar=False)
        evals, evecs = np.linalg.eig(cov)
        curvature[i] = np.min(evals) / np.sum(evals)

    return curvature

使用示例：

import open3d as o3d

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

# 获取点云数组
points = np.asarray(pcd.points)

# 计算主曲率
curvature = compute_curvature(points)

# 可视化
pcd.colors = o3d.utility.Vector3dVector(np.zeros_like(points))
pcd.colors[curvature > 0.1] = [1, 0, 0] # 根据曲率值设置颜色
o3d.visualization.draw_geometries([pcd])

其中，`k`参数表示每个点的邻居数量，可以根据具体情况进行调整。