提出逐轮生成高质量匹配的新方法，显著提升极端噪声下的3D配准鲁棒性。
Progressive Correspondence Regenerator for Robust 3D Registration
- 逐轮生成匹配而非删除异常值，通过局部一致性校正提升匹配质量
- 在极端异常值比例下获得10倍于传统方法的正确匹配数
- 适用于弱特征场景，适合高鲁棒性3D配准任务
高质量对应关系对鲁棒3D配准至关重要。现有对应关系优化方法多采用异常值剔除范式，在极端异常值比例下要么无法准确识别真实对应，要么剔除过多正确对应导致支持不足。为此，本文提出一种名为Regor的新方法，即渐进式对应关系再生器，可在大量异常值下生成更高品质的匹配。每轮迭代中，先通过先验引导的局部分组与广义互匹配生成局部区域对应；随后引入中心感知三点一致性机制进行局部对应校正，而非剔除；最后通过全局优化获取精确对应。经多次迭代，可生成大量高质量对应。大量实验表明，Regor显著优于现有异常值剔除方法，尤其在极端条件下，正确对应数量达传统方法的10倍，即使在弱特征情况下仍能实现鲁棒配准。代码将公开。
原文摘要 · Abstract (English)
Obtaining enough high-quality correspondences is crucial for robust registration. Existing correspondence refinement methods mostly follow the paradigm of outlier removal, which either fails to correctly identify the accurate correspondences under extreme outlier ratios, or select too few correct correspondences to support robust registration. To address this challenge, we propose a novel approach named Regor, which is a progressive correspondence regenerator that generates higher-quality matches whist sufficiently robust for numerous outliers. In each iteration, we first apply prior-guided local grouping and generalized mutual matching to generate the local region correspondences. A powerful center-aware three-point consistency is then presented to achieve local correspondence correction, instead of removal. Further, we employ global correspondence refinement to obtain accurate correspondences from a global perspective. Through progressive iterations, this process yields a large number of high-quality correspondences. Extensive experiments on both indoor and outdoor datasets demonstrate that the proposed Regor significantly outperforms existing outlier removal techniques. More critically, our approach obtain 10 times more correct correspondences than outlier removal methods. As a result, our method is able to achieve robust registration even with weak features. The code will be released.
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