importopen3daso3dimportnumpyasnpdefload_point_clouds(source_path,target_path):source=o3d.io.read_point_cloud(source_path)target=o3d.io.read_point_cloud(target_path)returnsource,targetdeficp_registration(source,target):threshold=0.02# 最大匹配距离reg_icp=o3d.pipelines.registration.registration_icp(...
importopen3daso3d# 加载点云数据source=o3d.io.read_point_cloud("source.ply")target=o3d.io.read_point_cloud("target.ply")# 可视化点云o3d.visualization.draw_geometries([source,target])# 粗配准threshold=0.02trans_init=np.eye(4)reg_p2p=o3d.pipelines.registration.registration_icp(source,target,t...
Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD) - jjcao/probreg
Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD) - neka-nat/probreg
viewer.addPointCloud(cloud, "sample cloud") viewer.spin() 3. 点云配准 PCL库提供了多种点云配准算法,包括ICP、NDT(Normal Distributions Transform)、RANSAC等。以下是使用ICP算法进行点云配准的示例: # 创建ICP对象 icp = pcl.registration.IterativeClosestPoint.PointXYZ() ...
reg_p2p = o3d.pipelines.registration.registration_icp( source, target, threshold, trans_init, o3d.pipelines.registration.TransformationEstimationPointToPoint()) return reg_p2p.transformation 读取两个点云 source = o3d.io.read_point_cloud("source.ply") ...
2、下采样:可以使用Open3D.geometry.PointCloud类中的voxel_down_sample方法对点云进行下采样操作,以减少点的数量。 voxel_size = 0.05 # 体素大小 downsampled_pcd = pcd.voxel_down_sample(voxel_size) 3、配准:可以使用Open3D.registration模块中的函数对两个点云进行配准操作,如ICP(Iterative Closest Point)算...
[0,0,0,1]]) reg_p2p = o3d.registration.registration_icp( source, target, threshold, trans_init, o3d.registration.TransformationEstimationPointToPoint(), o3d.registration.ICPConvergenceCriteria(relative_fitness=1.000000e-06, relative_rmse=1.000000e-06,max_iteration=50)) source.transform(reg_p2p....
estimator = o3d.pipelines.registration.TransformationEstimatorPointToPlane() # 平面估计器,用于计算平面法向量和截距 ransac_result = pcd.segment_plane(distance_threshold, ransac_num_iterations, max_correspondence_distance, estimator) # 执行RANSAC分割算法 ...
# 加载第一个点云 pcd1 = o3d.io.read_point_cloud("path_to_first_point_cloud.ply") # 加载第二个点云 pcd2 = o3d.io.read_point_cloud("path_to_second_point_cloud.ply") 3. 对点云进行配准 点云配准是拼接的关键步骤,它计算两个点云之间的变换矩阵,使得它们能够对齐。Open3D提供了多种配准...