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BRIEF RESEARCH REPORT article

Front. Phys.
Sec. Interdisciplinary Physics
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1474797
This article is part of the Research Topic Advanced Signal Processing Techniques in Radiation Detection and Imaging, Volume II View all articles

Outdoor Large Scene 3D Point Cloud Reconstruction Method Based on Transformer

Provisionally accepted
Fangzhou Tang Fangzhou Tang 1Shuting Zhang Shuting Zhang 1*Bocheng Zhu Bocheng Zhu 1*Junren Sun Junren Sun 1,2*
  • 1 Peking University, Beijing, China
  • 2 School of Mechanical and Information Engineering, China University of Mining and Technology, Beijing, China

The final, formatted version of the article will be published soon.

    3D point clouds collected by low-channel Light Detection and Ranging (LiDAR) are relatively sparse, while high-channel LiDAR is costly for application. To address this issue, an outdoor large scenes point cloud reconstruction (LSPCR) technique based on Transformer is proposed. LSPCR first projects the original sparse 3D point cloud onto a 2D range image, then enhances the resolution in the vertical direction of the 2D range image, and finally converts the highresolution range image back into a 3D point cloud to obtain the reconstructed point cloud data. Experiments on the real-world KITTI dataset show that LSPCR achieves an average accuracy improvement of over 60% compared to non-deep learning algorithms, and it achieves better performance compared to latest deep learning algorithms. Therefore, LSPCR can serve as an effective solution for sparse point cloud reconstruction, effectively addressing the challenge of obtaining high-resolution LiDAR point clouds.

    Keywords: lidar, point cloud, transformer, reconstruction, Autonomous

    Received: 02 Aug 2024; Accepted: 04 Sep 2024.

    Copyright: © 2024 Tang, Zhang, Zhu and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Shuting Zhang, Peking University, Beijing, China
    Bocheng Zhu, Peking University, Beijing, China
    Junren Sun, Peking University, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.