  3D single object tracking plays an essential role in many applications, such
as autonomous driving. It remains a challenging problem due to the large
appearance variation and the sparsity of points caused by occlusion and limited
sensor capabilities. Therefore, contextual information across two consecutive
frames is crucial for effective object tracking. However, points containing
such useful information are often overlooked and cropped out in existing
methods, leading to insufficient use of important contextual knowledge. To
address this issue, we propose CXTrack, a novel transformer-based network for
3D object tracking, which exploits ConteXtual information to improve the
tracking results. Specifically, we design a target-centric transformer network
that directly takes point features from two consecutive frames and the previous
bounding box as input to explore contextual information and implicitly
propagate target cues. To achieve accurate localization for objects of all
sizes, we propose a transformer-based localization head with a novel center
embedding module to distinguish the target from distractors. Extensive
experiments on three large-scale datasets, KITTI, nuScenes and Waymo Open
Dataset, show that CXTrack achieves state-of-the-art tracking performance while
running at 34 FPS.
