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
this https URL

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％。