用图像转换提升小重叠点云注册精度
MT-PCR: Leveraging Modality Transformation for Large-Scale Point Cloud Registration with Limited Overlap
- 将3D点云转为鸟瞰图，用2D关键点匹配提高效率
- 在8个平方公里级场景中实现高精度注册，重叠率低仍稳定
- 适合地面与航拍激光扫描的海量点云配准任务
大规模点云注册在重叠度有限的情况下因计算负担和数据获取受限而极具挑战。为此，我们提出基于模态转换的点云注册方法MT-PCR。MT-PCR利用鸟瞰图（BEV）捕捉最大重叠信息以提升精度，并借助图像提供互补空间特征。具体而言，将3D点云转换为BEV图像，通过2D关键点提取与匹配估计对应关系，再通过逆映射将2D匹配结果转换回3D点云。我们在GrAco数据集上对地面激光扫描与航拍激光扫描的点云注册进行了测试，涵盖8个低重叠、平方公里级注册场景。实验表明，与常用方法相比，MT-PCR在大规模弱重叠场景下具备更优的准确性和鲁棒性。
原文摘要 · Abstract (English)
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.
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