An efficient 3D point cloud classification approach via persistent homology

Point cloud is a critically important geometric data structure, and researchers have increasingly focused on and achieved promising results in terms of point cloud processing since PointNet's pioneering work. However, most previous methods only represent the shape of point clouds through coordinates or normal vectors, neglecting the intrinsic geometric and topological properties of this data structure. In this paper, we present an effective point cloud analysis approach which is using topological information. By employing a simplified version of the PointNet++(SSG version), we conduct benchmark experiments on the ModelNet40 dataset to evaluate TPA's performance in the classification task. Our improved method can still directly process point clouds, as the topological invariants ensure the permutation invariance of the input points. Simulation results show that the topological approach based on persistent homology can effectively provide topological structural features and improve the accuracy of the models.