PillarTrack: Redesigning Pillar-based Transformer Network for Single Object Tracking on Point Clouds
<|reference_start|>PillarTrack: Redesigning Pillar-based Transformer Network for Single Object Tracking on Point Clouds: 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.<|reference_end|>
@article{xu2024pillartrack:,
    title={PillarTrack: Redesigning Pillar-based Transformer Network for Single
  Object Tracking on Point Clouds},
    author={Weisheng Xu, Sifan Zhou, Zhihang Yuan},
    journal={arXiv preprint arXiv:2404.07495},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07495},
    primaryClass={cs.CV}
}