Open3d使用FLANN构建KDTree以便进行快速最近邻检索。 从点云中建立KDTree 下面的代码读取一个点云并且构建一个KDTree。这是下面最邻近查询的需处理步骤。 代码语言:javascript 复制 print("Testing kdtree in open3d ...")print("Load a point cloud and paint it gray.")pcd=o3d.io.read_point_cloud(".....
KDTreeFlann(pcd): 建立KD树 search_knn_vector_3d(search_Pt, k) K 近邻搜索 search_radius_vector_3d(search_Pt, radius) 半径搜索 search_hybrid_vector_3d(search_pt, radius, max_nn) 混合邻域搜索,返回半径 radius 内不超过 max_nn 个近邻点 2.代码示例 import open3d as o3d import numpy as np...
number = len(self.pcd.points) kdtree = o3d.geometry.KDTreeFlann(self.pcd) self.point_neighbours = np.zeros((number, self.neighbour_number)) for ik in range(number): [_, idx, _] = kdtree.search_knn_vector_3d(self.pcd.points[ik], self.neighbour_number) # K近邻搜索 self.point_nei...
downpcd=pcd.voxel_down_sample(voxel_size=0.05) downpcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1,max_nn=30)) o3d.visualization.draw_geometries([downpcd], zoom=0.3412, front=[0.4257, -0.2125, -0.8795], lookat=[2.6172, 2.0475, 1.532], up=[-0.0694, -0....
importopen3daso3d# 读取点云数据pcd=o3d.io.read_point_cloud("point_cloud.pcd")# 创建KD树kd_tree=o3d.geometry.KDTreeFlann(pcd)# 设定参数seed_index=5000threshold=0.02# 区域生长region_growing=o3d.geometry.RegionGrowing(pcd,kd_tree,seed_index,threshold)# 提取区域region_cloud=region_growing.segmen...
# 3. Poisson泊松曲面重建pcd_rc3 = deepcopy(pcd)pcd_rc3.translate([0, 0, 50])pcd_rc3.estimate_normals( # 法向量计算search_param=open3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))mesh_rc3, densities = open3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd_rc3, de...
7. kdtree 8. 颜色映射 8.1. 刚性优化 8.2. 非刚性优化 9. 画3D矩形框方法: 10. 连续帧点云流可视化方法: 官方文档 1. PointCloud 与 io操作 homepage_api: PointCloud 常用方法: print(pcd) -> 输出模型的points/faces数量 属性: normals
search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30) # 计算点云的法线 pcd.estimate_normals(search_param=search_param) # 可选:确保法线的方向一致性 # pcd.orient_normals_consistent_tangent_plane(30) # 可视化原始点云和计算得到的法线 ...
(search_param=open3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))# 点云三角化mesh = open3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(pcd, alpha=5)# 可视化点云列表pcd.translate((50, 50, 50), relative=False)open3d.visualization.draw_geometries([pcd, mesh],...
print(":: Estimate normal with search radius %.3f."% voxel_size)pcd.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size, max_nn=30)) print(":: Downsample with a voxel size %.3f."% voxel_size)pcd_down = pcd.voxel_down_sample(voxel_s...