DeFlow: Decoder of Scene Flow Network in Autonomous Driving
Qingwen Zhang Yi Yang Heng Fang Ruoyu Geng Patric Jensfelt
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 open-sourced
at https://github.com/KTH-RPL/deflow.