  LiDAR-based 3D single object tracking (3D SOT) is a critical issue in
robotics and autonomous driving. It aims to obtain accurate 3D BBox from the
search area based on similarity or motion. However, existing 3D SOT methods
usually follow the point-based pipeline, where the sampling operation
inevitably leads to redundant or lost information, resulting in unexpected
performance. To address these issues, we propose PillarTrack, a pillar-based 3D
single object tracking framework. Firstly, we transform sparse point clouds
into dense pillars to preserve the local and global geometrics. Secondly, we
introduce a Pyramid-type Encoding Pillar Feature Encoder (PE-PFE) design to
help the feature representation of each pillar. Thirdly, we present an
efficient Transformer-based backbone from the perspective of modality
differences. Finally, we construct our PillarTrack tracker based above designs.
Extensive experiments on the KITTI and nuScenes dataset demonstrate the
superiority of our proposed method. Notably, our method achieves
state-of-the-art performance on the KITTI and nuScenes dataset and enables
real-time tracking speed. We hope our work could encourage the community to
rethink existing 3D SOT tracker designs.We will open source our code to the
research community in https://github.com/StiphyJay/PillarTrack.
