VLM Daily Papers

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

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 proposal. Extensive experiments on the large-scale NuScenes benchmark show that TrajTrack achieves new state-of-the-art performance, dramatically improving tracking precision by 4.48% over a strong baseline while running at 56 FPS. Besides, we also demonstrate the strong generalizability of TrajTrack across different base trackers. Video is available at https://www.bilibili.com/video/BV1ahYgzmEWP.

TLDR: The paper introduces TrajTrack, a novel trajectory-based paradigm for efficient LiDAR-based 3D single object tracking, achieving state-of-the-art performance by implicitly learning motion continuity from historical bounding box trajectories without additional point cloud inputs.

TLDR: 该论文介绍了一种名为 TrajTrack 的新型基于轨迹的范例，用于高效的基于激光雷达的 3D 单目标跟踪。它通过从历史边界框轨迹中隐式学习运动连续性来实现最先进的性能，而无需额外的点云输入。

Authors: BaiChen Fan, Sifan Zhou, Jian Li, Shibo Zhao, Muqing Cao, Qin Wang