LEARNING SCENE FLOW FROM 3D POINT CLOUDS WITH CROSS-TRANSFORMER AND GLOBAL MOTION CUES

Scene flow estimation is critical for real-world vision problems such as autonomous driving and augmented reality. Due to the popularity of 3D LiDAR sensors, scene flow estimation from 3D point clouds arouses increasing attention. Existing methods usually use a flow embedding-based layer to find correspondences between point pairs. However, only using a flow embedding-based layer is not enough to model the global mutual relationship between two features due to local matching. In this paper, we introduce a cross-transformer to capture more reliable dependencies for point pairs. Moreover, a global motion-aware module is adopted to learn large displacements with a non-local approach. The experimental results demonstrate that the proposed method achieves comparable performance on public datasets and confirm the effectiveness of exploiting the cross-transformer and global motion cues for scene flow estimation.