这个代码段将输出两个点集之间的Chamfer Distance值。 需要注意的是,上述实现方式虽然直观但效率较低,特别是对于大规模点集。在实际应用中,可以考虑使用更高效的算法,如KD-Tree来加速最近邻搜索过程。此外,如果你在处理三维点云数据,并且希望利用GPU加速计算,还可以考虑使用PyTorch等框架提供的Chamfer Distance实现。
As it is using pyTorch'sJIT compilation, there are no additional prerequisite steps that have to be taken. Simply import the module as shown below; CUDA and C++ code will be compiled on the first run. Usage fromchamfer_distanceimportChamferDistancechamfer_dist=ChamferDistance()#...# points an...
I updated the package to use a wrapper around the Pytorch3D package chamfer distance due to some gradients bugs in the original code. Please update to the new version if you face any issues. The only requirements are PyTotch and Pytorch3D with cuda support: ...
pytorch——计算两个等大无序点云中的距离Earth Mover Distance 目前要做两个无序点集之间的相似性计算,在看过Chamfer Distance后,个人觉得CD的计算方式决定了其无法处理两个点集整体分布差异大,但是局部有部分点距离很近的情况,而这种情况在点集中是一定可能出现的,因此使用EMD可以保证每个点集都有一一对应的整体...
Pytorch Chamfer Distance. Include aCUDAversion, and aPYTHONversion with pytorch standard operations. NB : In this depo, dist1 and dist2 are squared pointcloud euclidean distances, so you should adapt thresholds accordingly. F - Score CUDA VERSION ...