Large-scale scene point cloud registration with limited overlap is a challenging task due to computational load and constrained data acquisition. To tackle these issues, we propose a point cloud registration method, MT-PCR, based on Modality Transformation. MT-PCR leverages a BEV capturing the maximal overlap information to improve the accuracy and utilizes images to provide complementary spatial features. Specifically, MT-PCR converts 3D point clouds to BEV images and eastimates correspondence by 2D image keypoints extraction and matching. Subsequently, the 2D correspondence estimates are then transformed back to 3D point clouds using inverse mapping. We have applied MT-PCR to Terrestrial Laser Scanning and Aerial Laser Scanning point cloud registration on the GrAco dataset, involving 8 low-overlap, square-kilometer scale registration scenarios. Experiments and comparisons with commonly used methods demonstrate that MT-PCR can achieve superior accuracy and robustness in large-scale scenes with limited overlap.
View on arXiv@article{wu2025_2503.12833,
title={ MT-PCR: Leveraging Modality Transformation for Large-Scale Point Cloud Registration with Limited Overlap },
author={ Yilong Wu and Yifan Duan and Yuxi Chen and Xinran Zhang and Yedong Shen and Jianmin Ji and Yanyong Zhang and Lu Zhang },
journal={arXiv preprint arXiv:2503.12833},
year={ 2025 }
}