Constructing Diverse Inlier Consistency for Partial Point Cloud Registration

Abstract

Partial point cloud registration aims to align partial scans into a shared coordinate system. While learning-based partial point cloud registration methods have achieved remarkable progress, they often fail to take full advantage of the relative positional relationships both within (intra-) and between (inter-) point clouds. This oversight hampers their ability to accurately identify overlapping regions and search for reliable correspondences. To address these limitations, a diverse inlier consistency (DIC) method has been proposed that adaptively embeds the positional information of a reliable correspondence in the intra- and inter-point cloud. Firstly, a diverse inlier consistency-driven region perception (DICdRP) module is devised, which encodes the positional information of the selected correspondence within the intra-point cloud. This module enhances the sensitivity of all points to overlapping regions by recognizing the position of the selected correspondence. Secondly, a diverse inlier consistency-aware correspondence search (DICaCS) module is developed, which leverages relative positions in the inter-point cloud. This module studies an inter-point cloud DIC weight to supervise correspondence compatibility, allowing for precise identification of correspondences and effective outlier filtration. Thirdly, diverse information is integrated throughout our framework to achieve a more holistic and detailed registration process. Extensive experiments on object-level and scene-level datasets demonstrate the superior performance of the proposed algorithm. The code is available at https://github.com/yxzhang15/DIC .