AUTHOR=Tang Fangzhou , Zhang Shuting , Zhu Bocheng , Sun Junren TITLE=Outdoor large-scene 3D point cloud reconstruction based on transformer JOURNAL=Frontiers in Physics VOLUME=12 YEAR=2024 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2024.1474797 DOI=10.3389/fphy.2024.1474797 ISSN=2296-424X ABSTRACT=

3D point clouds collected by low-channel light detection and ranging (LiDAR) are relatively sparse compared to high-channel LiDAR, which is considered costly. To address this, an outdoor large-scene point cloud reconstruction (LSPCR) technique based on transformer is proposed in this study. The LSPCR approach first projects the original sparse 3D point cloud onto a 2D range image; then, it enhances the resolution in the vertical direction of the 2D range image before converting the high-resolution range image back to a 3D point cloud as the final reconstructed point cloud data. Experiments were performed on the real-world KITTI dataset, and the results show that LSPCR achieves an average accuracy improvement of over 60% compared to non-deep-learning algorithms; it also achieves better performance compared to the latest deep-learning algorithms. Therefore, LSPCR is an effective solution for sparse point cloud reconstruction and addresses the challenges associated with high-resolution LiDAR point clouds.