3D single object tracking (SOT) is an important and challenging task for the autonomous driving and mobile robotics. Most existing methods perform tracking between two consecutive frames while ignoring the motion patterns of the target over a series of frames, which would cause performance degradation in the scenes with sparse points. To break through this limitation, we introduce Sequence-to-Sequence tracking paradigm and a tracker named SeqTrack3D to capture target motion across continuous frames. Unlike previous methods that primarily adopted three strategies: matching two consecutive point clouds, predicting relative motion, or utilizing sequential point clouds to address feature degradation, our SeqTrack3D combines both historical point clouds and bounding box sequences. This novel method ensures robust tracking by leveraging location priors from historical boxes, even in scenes with sparse points. Extensive experiments conducted on large-scale datasets show that SeqTrack3D achieves new state-of-the-art performances, improving by 6.00% on NuScenes and 14.13% on Waymo dataset. The code will be made public at https://github.com/aron-lin/seqtrack3d.

SeqTrack3D是一种基于Sequence-to-Sequence追踪范式的3D单目标追踪器，通过结合历史点云和边界框序列以确保鲁棒追踪，即使在稀疏点场景中也能利用历史边界框位置先验知识。在大规模数据集上的广泛实验表明，SeqTrack3D实现了最新的最佳性能，NuScenes上提高了6.00％，Waymo数据集提高了14.13％。

SeqTrack3D：探索序列信息以提升稳健的3D点云跟踪