As a key step in Six-Degree-of-Freedom (6DoF) point cloud registration, 3D keypoint technique aims to extract matches or inliers from random correspondences between the two keypoint sets. The major challenge in 3D keypoint techniques is the high ratio of mismatched or outliers in random correspondences in real-world point cloud registration. In this paper, we present a novel inlier extraction method, which is based on Supervoxel Guidance and Game Theory optimization (SGGT), to extract reliable inliers and apply for point cloud registration. Specifically, to reduce the scale of keypoint correspondences, we first construct powerful groups of keypoint correspondences by introducing supervoxels, which involves 3D spatial homogeneity. Second, to select promising combined groups, we present a novel ‘fit-and-remove’ strategy by incorporating 3D local transformation constraints. Third, to extract purer inliers for point cloud registration, we propose a grouping non-cooperative game algorithm, which considers the relationship between the combined groups. The proposed SGGT, by eliminating the mismatched combined groups globally, avoids the false combined groups that lead to the failed estimation of rigid transformations. Experimental results show that when processing on large keypoint sets, the proposed SGGT is over 100 times more efficient compared to the stat-of-the-art, while keeping the similar accuracy.

作为六自由度（6DoF）点云注册的关键步骤，3D关键点技术旨在从两个关键点集之间的随机对应关系中提取匹配项或内部值。3D关键点技术的主要挑战是现实点云注册中随机对应关系中不匹配或离群值的比例很高。在本文中，我们提出了一种基于Supervoxel制导和博弈论优化（SGGT）的新颖的离群点提取方法，以提取可靠的离群点并申请点云配准。具体来说，为减少关键点对应关系的规模，我们首先通过引入涉及3D空间均匀性的超级体素来构造强大的关键点对应关系组。其次，选择有前途的合并组，通过结合3D局部变换约束，我们提出了一种新颖的“拆装”策略。第三，为提取更纯净的点数用于点云配准，我们提出了一种分组非合作博弈算法，该算法考虑了合并组之间的关系。拟议的SGGT通过全局消除不匹配的组合组，避免了导致对刚性变换进行估计失败的错误的组合组。实验结果表明，在大型关键点集上进行处理时，所提出的SGGT的效率是最新技术的100倍以上，同时保持了相似的准确性。通过全局消除不匹配的组合组，可以避免导致刚性转换估计失败的错误组合组。实验结果表明，在大型关键点集上进行处理时，所提出的SGGT的效率是最新技术的100倍以上，同时保持了相似的准确性。通过全局消除不匹配的组合组，可以避免导致刚性转换估计失败的错误组合组。实验结果表明，在大型关键点集上进行处理时，所提出的SGGT的效率是最新技术的100倍以上，同时保持了相似的准确性。