  3D Single Object Tracking (SOT) is a fundamental task of computer vision,
proving essential for applications like autonomous driving. It remains
challenging to localize the target from surroundings due to appearance
variations, distractors, and the high sparsity of point clouds. To address
these issues, prior Siamese and motion-centric trackers both require elaborate
designs and solving multiple subtasks. In this paper, we propose BEVTrack, a
simple yet effective baseline method. By estimating the target motion in
Bird's-Eye View (BEV) to perform tracking, BEVTrack demonstrates surprising
simplicity from various aspects, i.e., network designs, training objectives,
and tracking pipeline, while achieving superior performance. Besides, to
achieve accurate regression for targets with diverse attributes (e.g., sizes
and motion patterns), BEVTrack constructs the likelihood function with the
learned underlying distributions adapted to different targets, rather than
making a fixed Laplacian or Gaussian assumption as in previous works. This
provides valuable priors for tracking and thus further boosts performance.
While only using a single regression loss with a plain convolutional
architecture, BEVTrack achieves state-of-the-art performance on three
large-scale datasets, KITTI, NuScenes, and Waymo Open Dataset while maintaining
a high inference speed of about 200 FPS. The code will be released at
https://github.com/xmm-prio/BEVTrack.
