3D point cloud analysis has received increasing attention in recent years, however, the diversity and availability of point cloud datasets are still limited. We therefore present PointCutMix, a simple but effective method for augmentation in point cloud. In our method, after finding the optimal assignment between two point clouds, we replace some points in one point cloud by its counterpart point in another point cloud. Our strategy consistently and significantly improves the performance across various models and datasets. Surprisingly, when it is used as a defense method, it shows far superior performance to the SOTA defense algorithm. The code is available at:https://github.com/cuge1995/PointCutMix
翻译:3D点云分析近年来受到越来越多的注意,然而,点云数据集的多样性和可用性仍然有限。 因此,我们提出点云组合,这是在点云中扩增的简单而有效的方法。 在我们的方法中,在找到两个点云之间的最佳分配后,我们用另一个点云中的对等点替换了两个点云中的部分点。我们的策略一贯且显著地改善了各种模型和数据集的性能。令人惊讶的是,当它被用作防御方法时,它显示了远优于SOTA防御算法的性能。代码可在以下网址查阅:https://github.com/cugue1995/PointCutMix。