Unidirectional Point-Voxel Fusion for Enhanced 3D Single Object Tracking

Abstract

Sparse point-based trackers struggle with texture-less and incomplete point clouds. Conversely, dense voxel-based trackers have richer spatial and semantic information, but filtering out interference from complex backgrounds remains a challenge. Additionally, there is still a gap between point and voxel-based trackers in exploiting their complementary strengths. To address these issues, we propose UTracker, which uses unidirectional point-voxel fusion to construct a bridge between point and voxel tracking features, enabling them to complement and enhance each other. Specifically, we design template-enhanced unidirectional attention (TEUA) and historical template fusion (HTF), which enable unidirectional interaction from historical templates to the search area in the point branch, retaining the pure template features. Then, a point-guided adaptive feature transformer (PGAFT) is developed to unidirectionally enhance the interaction between point and voxel features. Extensive experiments demonstrate that UTracker achieves superior performance, reaching an average accuracy of 89.5%, 72.58%, and 63.4% on the KITTI, NuScenes, and Waymo Open Dataset, respectively.