Incorporating Rotation Invariance with Non-invariant Networks for Point Clouds

Rotation invariance is a fundamental requirement of point cloud processing when input point clouds are not aligned. Many non-invariant networks performing well on aligned point clouds do not perform equivalent to rotated ones. Thus non-invariant and invariant networks are developed separately and only benefit a little from each other, leading to repetitive and wasteful research efforts. In this paper, we aim to bridge this gap and incorporate rotation invariance with non-invariant networks for point clouds. To this end, we propose a novel rotation invariant learning method based on efficient invariant poses (EIPs). EIPs do not rely on novel features or operations. Instead, they only rotate input point clouds into invariant poses and apply non-invariant networks in feature processing. As the name implies, EIPs have negligible complexities (efficient) and solid theoretical foundations (invariant). Experimental results demonstrate that EIPs have competitive performances on several tasks. Without using new features or operations, EIPs yield the best results on ScanObjectNN (PB T50 RS) classification and ShapeNetPart segmentation task. Our code is available at: https://github.com/JaronTHU/EIP.