Learning Adaptive Conceptual Prototypes for 3D Single Object Tracking

Jie Xiao, Yinchao Ma, Wenfei Yang, Tianzhu Zhang

Abstract: 3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. It remains a challenging problem due to the incompleteness and the sparsity of points caused by occlusion and limited sensor capabilities. Previous methods design various modules to propagate target perceptual cues to the current frame for target localization. However, perceptual cues may contain less information for occluded or distant objects, which brings great challenges to estimating the target state accurately. To address the above limitations, we propose a novel 3D SOT framework based on the adaptive conceptual prototypes named ACPTrack, which first learns the conceptual prototype from the prior knowledge of the category structure, and then associates weak perceptual cues with the learned conceptual prototypes to improve tracking performance. The proposed ACPTrack enjoys several merits. First, we propose a universal learning method of adaptive conceptual prototype, which can quickly adapt to target-specific structure with given perceptual cues. Second, we design two modules based on the conceptual prototype for structure completion and positioning refinement, which can exploit the rich structure information of the conceptual prototype to deal with sparse and incomplete targets for robust tracking. Third, our framework is generic and compatible with various 3D trackers and brings consistent performance gains. Extensive experiments validate that our method achieves competitive performance on three large-scale datasets.

External IDs:doi:10.1109/tcsvt.2025.3533155