DeFlow: Decoder of Scene Flow Network in Autonomous Driving

• Published in: 2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 2105 - 2111
• Main Authors: Zhang, Qingwen, Yang, Yi, Fang, Heng, Geng, Ruoyu, Jensfelt, Patric
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.05.2024
• Subjects: Estimation; Feature extraction; Point cloud compression; Real-time systems; Sensor fusion; Sensors; Three-dimensional displays
• DOI: 10.1109/ICRA57147.2024.10610278

Scene flow estimation determines a scene's 3D motion field, by predicting the motion of points in the scene, especially for aiding tasks in autonomous driving. Many networks with large-scale point clouds as input use voxelization to create a pseudo-image for real-time running. However, the voxelization process often results in the loss of point-specific features. This gives rise to a challenge in recovering those features for scene flow tasks. Our paper introduces DeFlow which enables a transition from voxel-based features to point features using Gated Recurrent Unit (GRU) refinement. To further enhance scene flow estimation performance, we formulate a novel loss function that accounts for the data imbalance between static and dynamic points. Evaluations on the Argoverse 2 scene flow task reveal that DeFlow achieves state-of-the-art results on large-scale point cloud data, demonstrating that our network has better performance and efficiency compared to others. The code is available at https://github.com/KTH-RPL/deflow.