BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View

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. The spatial information indicating objects' spatial adjacency across consecutive frames is crucial for effective object tracking. However, existing trackers typically employ point-wise representation with irregular formats, leading to insufficient use of this important spatial knowledge. As a result, these trackers usually require elaborate designs and solving multiple subtasks. In this paper, we propose BEVTrack, a simple yet effective baseline that performs tracking in Bird's-Eye View (BEV). This representation greatly retains spatial information owing to its ordered structure and inherently encodes the implicit motion relations of the target as well as distractors. To achieve accurate regression for targets with diverse attributes (\textit{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 Laplace 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.