RPPformer-Flow: Relative Position Guided Point Transformer for Scene Flow Estimation

Estimating scene flow for point clouds is one of the key problems in 3D scene understanding and autonomous driving. Recently the point transformer architecture has become a popular and successful solution for 3D computer vision tasks, e.g., point cloud object detection and completion, but its application to scene flow estimation is rarely explored. In this work, we provide a full transformer based solution for scene flow estimation. We first introduce a novel relative position guided point attention mechanism. Then to relax the memory consumption in practice, we provide an efficient implementation of our proposed point attention layer via matrix factorization and nearest neighbor sampling. Finally, we build a pyramid transformer, named RPPformer-Flow, to estimate the scene flow between two consecutive point clouds in a coarse-to-fine manner. We evaluate our RPPformer-Flow on the FlyingThings3D and KITTI Scene Flow 2015 benchmarks. Experimental results show that our method outperforms previous state-of-the-art methods with large margins.