Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations

Kewei Wang, Yizheng Wu, Jun Cen, Zhiyu Pan, Xingyi Li, Zhe Wang, Zhiguo Cao, Guosheng Lin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 14638-14647

Abstract


The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems wherein class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning the manual labeling of point cloud data is laborious and time-consuming. Therefore several annotation-efficient methods have been proposed to address this challenge. Although effective these methods rely on weak annotations or additional multi-modal data like images and the potential benefits inherent in the point cloud sequence are still underexplored. To this end we explore the feasibility of self-supervised motion prediction with only unlabeled LiDAR point clouds. Initially we employ an optimal transport solver to establish coarse correspondences between current and future point clouds as the coarse pseudo motion labels. Training models directly using such coarse labels leads to noticeable spatial and temporal prediction inconsistencies. To mitigate these issues we introduce three simple spatial and temporal regularization losses which facilitate the self-supervised training process effectively. Experimental results demonstrate the significant superiority of our approach over the state-of-the-art self-supervised methods. Code will be available.

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[bibtex]
@InProceedings{Wang_2024_CVPR, author = {Wang, Kewei and Wu, Yizheng and Cen, Jun and Pan, Zhiyu and Li, Xingyi and Wang, Zhe and Cao, Zhiguo and Lin, Guosheng}, title = {Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {14638-14647} }