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 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 this https URL.
Abstract (translated)
基于LiDAR的3D单目标跟踪（3D SOT）是机器人和自主系统中的关键任务。现有的方法通常遵循逐帧运动估计或序列处理范式。然而，两帧方法虽然效率高但缺乏长期的时间上下文，在稀疏或被遮挡场景中容易失效；而基于序列的方法通过处理多帧点云来获得鲁棒性，但会带来较大的计算开销。为解决这一困境，我们提出了一种新颖的轨迹导向范式及其具体实现——TrajTrack。 TrajTrack是一个轻量级框架，它通过对历史边界框轨迹进行隐式的运动连续性学习，增强了基础两帧追踪器的能力，并且无需额外、昂贵的点云输入即可完成。首先，它生成一个快速、明确的运动提案，然后通过一个隐式运动建模模块预测未来的轨迹路径，进一步优化和校正初始提案。 在大规模NuScenes基准测试中进行的大量实验表明，TrajTrack实现了新的最佳性能，在显著提高跟踪精度的同时（相较于强大的基线提高了4.48%），还能保持56 FPS的速度。此外，我们也展示了TrajTrack在不同基础追踪器上的强大泛化能力。视频可在该链接获取：[此处应填写具体的URL]。
URL
https://arxiv.org/abs/2509.11453
