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

alg/point_porj/archive/relative_code/CE.py

@@ -0,0 +1,54 @@
+import open3d as o3d
+import numpy as np
+
+
+def compute_curvature_open3d(pcd, radius=0.1, max_nn=30):
+    """
+    使用 Open3D 计算曲率
+
+    Args:
+        pcd: Open3D 点云对象
+        radius: 搜索半径
+        max_nn: 最大邻居数
+
+    Returns:
+        curvature: 曲率数组
+    """
+    # 估计法线
+    pcd.estimate_normals(
+        search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=radius, max_nn=max_nn)
+    )
+
+    # 计算曲率（Open3D 在估计法线时已计算）
+    # 曲率值存储在 pcd.point.normals 的第三个分量？
+    # 实际上 Open3D 需要单独计算
+
+    # 方法：使用 PCA 重新计算
+    points = np.asarray(pcd.points)
+    n_points = len(points)
+    curvature = np.zeros(n_points)
+
+    tree = o3d.geometry.KDTreeFlann(pcd)
+
+    for i in range(n_points):
+        [k, idx, _] = tree.search_radius_vector_3d(pcd.points[i], radius)
+
+        if k < 3:
+            curvature[i] = 1.0
+            continue
+
+        neighbors = points[idx]
+        centroid = np.mean(neighbors, axis=0)
+        centered = neighbors - centroid
+        cov = np.cov(centered.T)
+        eigenvalues = np.linalg.eigvalsh(cov)
+        eigenvalues = np.sort(eigenvalues)
+
+        curvature[i] = eigenvalues[0] / (eigenvalues.sum() + 1e-8)
+
+    return curvature
+
+
+# 使用示例
+pcd = o3d.io.read_point_cloud("pointcloud.pcd")
+curvature = compute_curvature_open3d(pcd, radius=0.05)