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
this https URL

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）。