Learning graph-based representations for scene flow estimation

Scene flow estimation is a fundamental task of autonomous driving. Compared with optical flow, scene flow can provide sufficient 3D motion information of the dynamic scene. With the increasing popularity of 3D LiDAR sensors and deep learning technology, 3D LiDAR-based scene flow estimation methods have achieved outstanding results on public benchmarks. Current methods usually adopt Multiple Layer Perceptron (MLP) or traditional convolution-like operation for feature extraction. However, the characteristics of point clouds are not exploited adequately in these methods, and thus some key semantic and geometric structures are not well captured. To address this issue, we propose to introduce graph convolution to exploit the structural features adaptively. In particular, multiple graph-based feature generators and a graph-based flow refinement module are deployed to encode geometric relations among points. Furthermore, residual connections are used in the graph-based feature generator to enhance feature representation and deep supervision of the graph-based network. In addition, to focus on short-term dependencies, we introduce a single gate-based recurrent unit to refine scene flow predictions iteratively. The proposed network is trained on the FlyingThings3D dataset and evaluated on the FlyingThings3D, KITTI, and Argoverse datasets. Comprehensive experiments show that all proposed components contribute to the performance of scene flow estimation, and our method can achieve potential performance compared to the recent approaches.