height=800,point_show_normal=True)pcd_origin.estimate_normals(search_param=o3d.geometry.KDTreeSear...
o3d.visualization.draw_geometries([downpcd]) Vertex normal estimation 点云的另一个基本操作是点法线估计。 print("Recompute the normal of the downsampled point cloud") downpcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid( radius=0.1, max_nn=30)) o3d.visualization.draw_geom...
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 o3d.visualization.draw_geometries([pcd], ...
print("3-2. Estimate normal.")source_down.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=radius * 2, max_nn=30))target_down.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=radius * 2, max_nn=30)) print("3-3. Applying colored point cloud registration")result_ic...
然后,我们使用`o3d.geometry.estimate_normals`函数来估计点云的法线信息。接下来,我们使用`uniform_down_sample`方法来进行非均匀下采样,并设置保留点的比例为0.2。最后,我们使用`o3d.io.write_point_cloud`函数将下采样后的点云保存到文件中。 8.总结 非均匀下采样是一种有效的方法来减少点云数据量,同时保留...
downpcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30)) // 设定估计法线的参数,搜索方法是kdtree(半径0.1米,最大邻居数量30) o3d.visualization.draw_geometries([downpcd], zoom=0.3412, front=[0.4257, -0.2125, -0.8795], ...
from open3d.open3d.geometry import voxel_down_sample,estimate_normals as a work around Hi, I tried your strategy and when calling "voxel_down_sample(pcd)" it shows error " voxel_down_sample(): incompatible function arguments." Do you have the similar issue before? Thanks! You need to gi...
`open3d.geometry.estimate_normals(pointcloud, search_param)`: 估计点云的法向量。 4. 三维重建: `open3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(pointcloud, alpha)`: 基于点云创建三角网格。 `open3d.geometry.voxel_grid.create_from_point_cloud(pointcloud, voxel_size)`: 基于点...
importopen3daso3dimportnumpyasnpimportmatplotlib.pyplotasplt# 加载点云数据defload_point_cloud(file_path):pcd=o3d.io.read_point_cloud(file_path)returnpcd# 计算法向量defcompute_normals(pcd):pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1,max_nn=30))returnpcd.no...
1.8. Vertex normal estimation downpcd.estimate_normals( search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30)) The two key arguments: radius = 0.1 max_nn = 30 specifies search radius and maximum nearest neighbor.