DeFlow: Decoder of Scene Flow Network in Autonomous Driving

• URL: http://arxiv.org/abs/2401.16122v1
• Date: Mon, 29 Jan 2024 12:47:55 GMT
• Title: DeFlow: Decoder of Scene Flow Network in Autonomous Driving
• Authors: Qingwen Zhang, Yi Yang, Heng Fang, Ruoyu Geng, Patric Jensfelt
• Abstract summary: Scene flow estimation determines a scene's 3D motion field, by predicting the motion of points in the scene. Many networks with large-scale point clouds as input use voxelization to create a pseudo-image for real-time running. Our paper introduces DeFlow which enables a transition from voxel-based features to point features using Gated Recurrent Unit (GRU) refinement.
• Score: 19.486167661795797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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