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

alg/point_porj/archive/relative_code/test5.py

@@ -0,0 +1,25 @@
+import trimesh
+import numpy as np
+import open3d as o3d
+import time
+# 1. 读取数据
+mesh_trimesh = trimesh.load("E:\point_distance\data\\40.stl")
+pcd_o3d = o3d.io.read_point_cloud("E:\point_distance\data\\40_noisy.ply")
+points = np.asarray(pcd_o3d.points)
+start_time = time.time()
+
+# ========== 1. 读取数据 ==========
+
+t0 = time.time()
+# 2. 使用 trimesh 计算每个点到 Mesh 的最近距离
+# trimesh 的 nearest 方法非常高效且精确
+closest_points, distances, triangle_ids = mesh_trimesh.nearest.on_surface(points)
+t1=time.time()
+
+# distances 即为所求的距离数组
+print(f"平均距离: {np.mean(distances)}")
+print(f"最大距离: {np.max(distances)}")
+print(f"  距离计算:   {t1 - t0:.4f} 秒")
+# 如果需要将结果存回 Open3D 点云进行可视化
+dists_o3d = o3d.utility.DoubleVector(distances.tolist())
+# 注意：Open3D 点云对象本身不直接存储标量距离字段用于内置可视化着色