MT-PCR: Leveraging Modality Transformation for Large-Scale Point Cloud Registration with Limited Overlap
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
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.
