ML-SemReg：利用多层语义一致性提升点云配准性能
ML-SemReg: Boosting Point Cloud Registration with Multi-level Semantic Consistency
AI总结：
针对现有点云配准方法在低重叠场景下效果不佳的问题，提出即插即用框架ML-SemReg，通过分组匹配和掩码匹配模块利用多层语义一致性解决两类错配，显著提升配准召回率与鲁棒性。
摘要：
当前点云配准领域的研究进展大多基于几何信息。尽管这类方法已取得可观成果，但仍难以应对低重叠度问题，进而限制了其实际应用。本文提出ML-SemReg，这是一种即插即用的点云配准框架，可充分挖掘语义信息的价值。我们的核心思路是：引入语义线索后，错配可分为类间错配与类内错配两类，利用多层语义一致性即可有效解决这两类问题。我们首先提出分组匹配模块以解决类间错配，输出的多个匹配组天然满足局部语义一致性。针对每个匹配组，我们进一步引入基于场景语义一致性的掩码匹配模块，以抑制类内错配。得益于这两个模块的协同作用，ML-SemReg生成的对应点对具有极高的内点率。大量实验验证了ML-SemReg的优异性能与鲁棒性：例如在KITTI数据集的困难场景下，当搭载我们的ML-SemReg后，MAC的配准召回率提升了近34个百分点。代码已开源至https://github.com/Laka-3DV/ML-SemReg。
英文摘要：
Recent advances in point cloud registration mostly leverage geometric information. Although these methods have yielded promising results, they still struggle with problems of low overlap, thus limiting their practical usage. In this paper, we propose ML-SemReg, a plug-and-play point cloud registration framework that fully exploits semantic information. Our key insight is that mismatches can be categorized into two types, i.e., inter- and intra-class, after rendering semantic clues, and can be well addressed by utilizing multi-level semantic consistency. We first propose a Group Matching module to address inter-class mismatching, outputting multiple matching groups that inherently satisfy Local Semantic Consistency. For each group, a Mask Matching module based on Scene Semantic Consistency is then introduced to suppress intra-class mismatching. Benefit from those two modules, ML-SemReg generates correspondences with a high inlier ratio. Extensive experiments demonstrate excellent performance and robustness of ML-SemReg, e.g., in hard-cases of the KITTI dataset, the Registration Recall of MAC increases by almost 34 percentage points when our ML-SemReg is equipped. Code is available at \url{https://github.com/Laka-3DV/ML-SemReg}
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