feat: initial commit - massive-cloud point cloud processing system

alg/point_porj/archive/relative_code/EN.py

@@ -0,0 +1,65 @@
+import open3d as o3d
+import numpy as np
+
+
+def compute_curvature_open3d(pcd, radius=0.05, max_nn=30):
+    """
+    使用 Open3D 计算点云曲率
+
+    Args:
+        pcd: Open3D 点云对象
+        radius: 搜索半径
+        max_nn: 最大邻居数
+
+    Returns:
+        curvatures: (N,) 曲率数组
+    """
+    # 估计法线（同时获取协方差矩阵信息）
+    pcd.estimate_normals(
+        search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=radius, max_nn=max_nn)
+    )
+
+    # 获取点云数据
+    points = np.asarray(pcd.points)
+    n_points = len(points)
+    curvatures = np.zeros(n_points)
+
+    # 构建 KD-Tree
+    tree = o3d.geometry.KDTreeFlann(pcd)
+
+    for i in range(n_points):
+        # 搜索邻域点
+        [k, idx, _] = tree.search_radius_vector_3d(pcd.points[i], radius)
+
+        if k < 3:
+            curvatures[i] = 1.0
+            continue
+
+        # 获取邻域点坐标
+        neighbors = points[idx]
+
+        # 计算协方差矩阵
+        centroid = np.mean(neighbors, axis=0)
+        centered = neighbors - centroid
+        cov = np.cov(centered.T)
+
+        # 特征值分解
+        eigenvalues = np.linalg.eigvalsh(cov)
+        eigenvalues = np.sort(eigenvalues)
+
+        # 计算曲率
+        curvatures[i] = eigenvalues[0] / (eigenvalues.sum() + 1e-8)
+
+    return curvatures
+
+
+# 使用示例
+pcd = o3d.io.read_point_cloud("E:\point_distance\data\PointCloud\\38.ply")
+curvatures = compute_curvature_open3d(pcd, radius=50, max_nn=30)
+
+# 将曲率作为颜色可视化
+colors = np.zeros((len(pcd.points), 3))
+colors[:, 0] = curvatures*3  # 红色通道表示曲率
+colors[:, 1] = 1 - curvatures*3  # 绿色通道为互补色
+pcd.colors = o3d.utility.Vector3dVector(colors)
+o3d.visualization.draw_geometries([pcd])