3D single object tracking is a key issue for robotics. In this paper, we propose a transformer module called Point-Track-Transformer (PTT) for point cloud-based 3D single object tracking. PTT module contains three blocks for feature embedding, position encoding, and self-attention feature computation. Feature embedding aims to place features closer in the embedding space if they have similar semantic information. Position encoding is used to encode coordinates of point clouds into high dimension distinguishable features. Self-attention generates refined attention features by computing attention weights. Besides, we embed the PTT module into the open-source state-of-the-art method P2B to construct PTT-Net. Experiments on the KITTI dataset reveal that our PTT-Net surpasses the state-of-the-art by a noticeable margin (~10\%). Additionally, PTT-Net could achieve real-time performance (~40FPS) on NVIDIA 1080Ti GPU. Our code is open-sourced for the robotics community at https://github.com/shanjiayao/PTT.

本篇研究文章提出基于点云的3D单目标跟踪的Transformer模块Point-Track-Transformer（PTT），其包含特征嵌入，位置编码和自注意力模块等三个模块，将该模块应用到现有的P2B方法上构建出PTT-Net，并在KITTI数据集上实验表明，该模型较现有方法提升了约10％左右的性能，并实现了实时性能（~40FPS）。

基于点云的三维单目标跟踪的点轨变换模块（Point-Track-Transformer）