ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds

• URL: http://arxiv.org/abs/2304.12589v1
• Date: Tue, 25 Apr 2023 05:46:24 GMT
• Title: ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds
• Authors: Xiangze Jia, Hui Zhou, Xinge Zhu, Yandong Guo, Ji Zhang, Yuexin Ma
• Abstract summary: We propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation. We predict scene motion via feature-level consistency between pillars in consecutive frames, which can eliminate the effect caused by noise points and view-changing point clouds in dynamic scenes.
• Score: 21.6511040107249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation. Different from usually adopted self-supervised strategies for data-level structure consistency, we predict scene motion via feature-level consistency between pillars in consecutive frames, which can eliminate the effect caused by noise points and view-changing point clouds in dynamic scenes. Specifically, we propose \textit{Soft Discriminative Loss} that provides the network with more pseudo-supervised signals to learn discriminative and robust features in a contrastive learning manner. We also propose \textit{Gated Multi-frame Fusion} block that learns valid compensation between point cloud frames automatically to enhance feature extraction. Finally, \textit{pillar association} is proposed to predict pillar correspondence probabilities based on feature distance, and whereby further predicts scene motion. Extensive experiments show the effectiveness and superiority of our \textbf{ContrastMotion} on both scene flow and motion prediction tasks. The code is available soon.

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