lipan
29f1d45d66
算法更新: delivery v1.2.1、平整度v1.5、测距v2.2、整车配准v1.3 适配器: vehicle-registration 新增、各算法 manifest 和 adapter 更新 后端: taskCadFiles/source 字段迁移、derivedPointClouds 注册、取消任务联动 前端: ElapsedTime 时长显示、两步算法流程、canRun 逻辑修复 文档: 新增5份算法性能分析报告
65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
import open3d as o3d
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import numpy as np
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def compute_curvature_open3d(pcd, radius=0.05, max_nn=30):
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"""
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使用 Open3D 计算点云曲率
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Args:
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pcd: Open3D 点云对象
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radius: 搜索半径
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max_nn: 最大邻居数
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Returns:
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curvatures: (N,) 曲率数组
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"""
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# 估计法线（同时获取协方差矩阵信息）
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pcd.estimate_normals(
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search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=radius, max_nn=max_nn)
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)
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# 获取点云数据
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points = np.asarray(pcd.points)
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n_points = len(points)
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curvatures = np.zeros(n_points)
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# 构建 KD-Tree
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tree = o3d.geometry.KDTreeFlann(pcd)
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for i in range(n_points):
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# 搜索邻域点
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[k, idx, _] = tree.search_radius_vector_3d(pcd.points[i], radius)
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if k < 3:
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curvatures[i] = 1.0
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continue
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# 获取邻域点坐标
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neighbors = points[idx]
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# 计算协方差矩阵
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centroid = np.mean(neighbors, axis=0)
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centered = neighbors - centroid
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cov = np.cov(centered.T)
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# 特征值分解
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eigenvalues = np.linalg.eigvalsh(cov)
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eigenvalues = np.sort(eigenvalues)
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# 计算曲率
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curvatures[i] = eigenvalues[0] / (eigenvalues.sum() + 1e-8)
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return curvatures
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# 使用示例
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pcd = o3d.io.read_point_cloud("E:\point_distance\data\PointCloud\\38.ply")
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curvatures = compute_curvature_open3d(pcd, radius=50, max_nn=30)
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# 将曲率作为颜色可视化
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colors = np.zeros((len(pcd.points), 3))
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colors[:, 0] = curvatures*3 # 红色通道表示曲率
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colors[:, 1] = 1 - curvatures*3 # 绿色通道为互补色
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pcd.colors = o3d.utility.Vector3dVector(colors)
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o3d.visualization.draw_geometries([pcd]) |