  Current 3D single object tracking approaches track the target based on a
feature comparison between the target template and the search area. However,
due to the common occlusion in LiDAR scans, it is non-trivial to conduct
accurate feature comparisons on severe sparse and incomplete shapes. In this
work, we exploit the ground truth bounding box given in the first frame as a
strong cue to enhance the feature description of the target object, enabling a
more accurate feature comparison in a simple yet effective way. In particular,
we first propose the BoxCloud, an informative and robust representation, to
depict an object using the point-to-box relation. We further design an
efficient box-aware feature fusion module, which leverages the aforementioned
BoxCloud for reliable feature matching and embedding. Integrating the proposed
general components into an existing model P2B, we construct a superior
box-aware tracker (BAT). Experiments confirm that our proposed BAT outperforms
the previous state-of-the-art by a large margin on both KITTI and NuScenes
benchmarks, achieving a 15.2% improvement in terms of precision while running
~20% faster.
