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 this https URL

我们提出了一种基于 pillar 的 3D 单物体跟踪框架 PillarTrack，通过将稀疏的点云转化为稠密的 pillar 来保留本地和全局几何特征，并引入了一种金字塔型编码 pillar 特征编码器（PE-PFE）设计以提高每个 pillar 的特征表示，并从模态差异的角度介绍了一种高效的基于 Transformer 的骨干网络。通过在 KITTI 和 nuScenes 数据集上进行广泛实验，我们的方法表现卓越，并实现了实时跟踪速度。我们希望我们的工作能够鼓励社区重新思考现有的 3D 单物体跟踪器设计。