PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds
release_xgp7xy6khrfk5o3fowzacpdca4
by
Jiayao Shan, Sifan Zhou, Zheng Fang, Yubo Cui
2021
Abstract
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.
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