SeqTrack3D: Exploring Sequence Information for Robust 3D Point Cloud
  Tracking
Yu Lin Zhiheng Li Yubo Cui Zheng Fang
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.