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...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 33; pp. 6535 - 6549
Main Authors: Zhang, Yu-Xin, Gui, Jie, Kwok, James Tin-Yau
Format: Journal Article
Language:English
Published: United States IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Coordinates
Data analysis
Electronic mail
Encoding
Feature extraction
Image coding
Image registration
inlier consistency
Iterative methods
Modules
Partial point cloud
Point cloud compression
position encoding
Registration
Singular value decomposition
Three-dimensional displays
Transformers
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary: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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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2024.3492700