解决跨源点云密度不一导致的配准难题，提升匹配与注册精度。
Cross-PCR: A Robust Cross-Source Point Cloud Registration Framework
- 设计密度鲁棒编码器提取稳定特征，应对不同源点云密度差异。
- 采用松到紧匹配策略，初始多对一生成后严格筛选高质量对应。
- 在3DCSR和3DMatch数据集上显著提升匹配与注册召回率，适配多种采样密度。
由于跨源点云存在密度不一致和分布差异，现有方法在跨源点云配准中表现不佳。本文提出一种密度鲁棒的特征提取与匹配方案，实现稳健且精确的跨源配准。为应对跨源数据间的密度差异，引入密度鲁棒编码器以提取不变特征；针对特征匹配困难及正确对应稀少的问题，采用‘松生成、严筛选’的松到紧匹配流程：先通过一对多策略生成初始对应关系，再经稀疏与密集匹配严格筛选高质量对应，实现鲁棒配准。在跨源3DCSR数据集的Kinect-LiDAR场景中，本方法使特征匹配召回率提升63.5个百分点（pp），注册召回率提升57.6 pp；同时在3DMatch上达到最佳性能，并在多种下采样密度下保持鲁棒性。
原文摘要 · Abstract (English)
Due to the density inconsistency and distribution difference between cross-source point clouds, previous methods fail in cross-source point cloud registration. We propose a density-robust feature extraction and matching scheme to achieve robust and accurate cross-source registration. To address the density inconsistency between cross-source data, we introduce a density-robust encoder for extracting density-robust features. To tackle the issue of challenging feature matching and few correct correspondences, we adopt a loose-to-strict matching pipeline with a ``loose generation, strict selection'' idea. Under it, we employ a one-to-many strategy to loosely generate initial correspondences. Subsequently, high-quality correspondences are strictly selected to achieve robust registration through sparse matching and dense matching. On the challenging Kinect-LiDAR scene in the cross-source 3DCSR dataset, our method improves feature matching recall by 63.5 percentage points (pp) and registration recall by 57.6 pp. It also achieves the best performance on 3DMatch, while maintaining robustness under diverse downsampling densities.
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