[2509.11453] Beyond Frame-wise Tracking: A Trajectory-based Paradigm for Efficient Point Cloud Tracking

Computer Science > Computer Vision and Pattern Recognition arXiv:2509.11453 (cs) [Submitted on 14 Sep 2025 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Beyond Frame-wise Tracking: A Trajectory-based Paradigm for Efficient Point Cloud Tracking Authors:BaiChen Fan, Yuanxi Cui, Jian Li, Qin Wang, Shibo Zhao, Muqing Cao, Sifan Zhou View a PDF of the paper titled Beyond Frame-wise Tracking: A Trajectory-based Paradigm for Efficient Point Cloud Tracking, by BaiChen Fan and 6 other authors View PDF HTML (experimental) Abstract:LiDAR-based 3D single object tracking (3D SOT) is a critical task in robotics and autonomous systems. Existing methods typically follow frame-wise motion estimation or a sequence-based paradigm. However, the two-frame methods are efficient but lack long-term temporal context, making them vulnerable in sparse or occluded scenes, while sequence-based methods that process multiple point clouds gain robustness at a significant computational cost. To resolve this dilemma, we propose a novel trajectory-based paradigm and its instantiation, TrajTrack. TrajTrack is a lightweight framework that enhances a base two-frame tracker by implicitly learning motion continuity from historical bounding box trajectories alone-without requiring additional, costly point cloud inputs. It first generates a fast, explicit motion proposal and then uses an implicit motion modeling module to predict the future trajectory, which in turn refines and corrects the initial prop...