  3D single object tracking has been a crucial problem for decades with
numerous applications such as autonomous driving. Despite its wide-ranging use,
this task remains challenging due to the significant appearance variation
caused by occlusion and size differences among tracked targets. To address
these issues, we present MBPTrack, which adopts a Memory mechanism to utilize
past information and formulates localization in a coarse-to-fine scheme using
Box Priors given in the first frame. Specifically, past frames with targetness
masks serve as an external memory, and a transformer-based module propagates
tracked target cues from the memory to the current frame. To precisely localize
objects of all sizes, MBPTrack first predicts the target center via Hough
voting. By leveraging box priors given in the first frame, we adaptively sample
reference points around the target center that roughly cover the target of
different sizes. Then, we obtain dense feature maps by aggregating point
features into the reference points, where localization can be performed more
effectively. Extensive experiments demonstrate that MBPTrack achieves
state-of-the-art performance on KITTI, nuScenes and Waymo Open Dataset, while
running at 50 FPS on a single RTX3090 GPU.
