Enhancing 3D Object Tracking Via Dual-Context Propagation and Temporal Context Fusion

Point cloud-based 3D single object tracking (3D SOT) plays a pivotal role in applications such as autonomous driving and robotic vision. Despite recent progress, most existing approaches rely solely on current-frame features for target localization. This approach overlooks temporal information that is crucial for robust tracking under occlusion, appearance variations, and sparse point clouds. In addition, the effectiveness of 3D SOT largely depends on the quality of feature fusion between the target template and the search region. Traditional fusion strategies often suffer from limited interaction capacity and weak discriminative representation. To address these challenges, we propose DT-Tracker, which performs multi-layer bidirectional feature interaction and temporal cue propagation to improve tracking robustness and feature discrimination capability. Specifically, we introduce a Dual-Context Propagation Network that applies bidirectional cross-attention across multiple layers between the template and search region, enabling deep semantic alignment and progressive feature refinement. Furthermore, we design a Temporal Context Fusion module that adaptively incorporates temporal cues from historical fusion features into the current frame, effectively improving resilience to occlusion and appearance drift. Extensive experiments on the KITTI and nuScenes datasets demonstrate that DT-Tracker achieves competitive results compared to existing representative methods.