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
26 lines
962 B
Python
26 lines
962 B
Python
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
|