Constructing Diverse Inlier Consistency for Partial Point Cloud Registration
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 betw...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 33(2024) vom: 12., Seite 6535-6549 |
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| Weitere Verfasser: | , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | 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 |
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| Beschreibung: | Date Revised 03.03.2025 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2024.3492700 |