SGOR：利用语义与几何信息实现鲁棒点云配准的外点去除方法
SGOR: Outlier Removal by Leveraging Semantic and Geometric Information for Robust Point Cloud Registration
AI总结：
针对现有语义点云配准方法依赖语义正确性、弱几何场景性能差的问题，提出SGOR外点去除方法，结合区域投票宽松语义一致性、语义-几何一致性与两阶段假设验证，大幅提升户外场景配准召回率与鲁棒性。
摘要：
本文提出一种充分利用几何与语义信息的新型外点去除方法，以实现鲁棒配准。当前基于语义的配准方法仅将语义用于生成点对点或实例级语义对应关系，存在两个问题：其一，这类方法高度依赖语义的正确性，在语义错误、语义稀疏的场景中表现不佳；其二，语义的使用仅局限于对应关系生成阶段，导致在弱几何场景下性能较差。为解决上述问题，一方面，我们提出基于区域投票的二次地面分割与宽松语义一致性策略，通过降低对单点语义的依赖，提升了对语义正确性的鲁棒性；另一方面，我们提出用于外点去除的语义-几何一致性机制，充分利用语义信息，显著提升了对应关系的质量。此外，我们还提出两阶段假设验证方法，解决了弱几何场景下变换矩阵选择错误的问题。在户外数据集上，本文方法展现出优异性能，配准召回率提升了22.5个百分点，在多种条件下均具备更优的鲁棒性。我们的代码已公开。
英文摘要：
In this paper, we introduce a new outlier removal method that fully leverages geometric and semantic information, to achieve robust registration. Current semantic-based registration methods only use semantics for point-to-point or instance semantic correspondence generation, which has two problems. First, these methods are highly dependent on the correctness of semantics. They perform poorly in scenarios with incorrect semantics and sparse semantics. Second, the use of semantics is limited only to the correspondence generation, resulting in bad performance in the weak geometry scene. To solve these problems, on the one hand, we propose secondary ground segmentation and loose semantic consistency based on regional voting. It improves the robustness to semantic correctness by reducing the dependence on single-point semantics. On the other hand, we propose semantic-geometric consistency for outlier removal, which makes full use of semantic information and significantly improves the quality of correspondences. In addition, a two-stage hypothesis verification is proposed, which solves the problem of incorrect transformation selection in the weak geometry scene. In the outdoor dataset, our method demonstrates superior performance, boosting a 22.5 percentage points improvement in registration recall and achieving better robustness under various conditions. Our code is available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
