Accurate Registration of Cross-Modality Geometry via Consistent Clustering

The registration of unitary-modality geometric data has been successfully explored over past decades. However, existing approaches typically struggle to handle cross-modality data due to the intrinsic difference between different models. To address this problem, in this article, we formulate the cro...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2024-07, Vol.30 (7), p.4055-4067
Main Authors: Zhao, Mingyang, Huang, Xiaoshui, Jiang, Jingen, Mou, Luntian, Yan, Dong-Ming, Ma, Lei
Format: Article
Language:English
Subjects:
3D reconstruction
adaptive fuzzy clustering
Algorithms
CAD
Centroids
Clustering
Clustering algorithms
Cross-modality geometry
Geometry
Laser radar
Outliers (statistics)
Point cloud compression
point cloud registration
Qualitative analysis
Registration
Robustness (mathematics)
Solid modeling
Tensors
Three-dimensional displays
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Summary:The registration of unitary-modality geometric data has been successfully explored over past decades. However, existing approaches typically struggle to handle cross-modality data due to the intrinsic difference between different models. To address this problem, in this article, we formulate the cross-modality registration problem as a consistent clustering process . First, we study the structure similarity between different modalities based on an adaptive fuzzy shape clustering, from which a coarse alignment is successfully operated. Then, we optimize the result using fuzzy clustering consistently, in which the source and target models are formulated as clustering memberships and centroids , respectively. This optimization casts new insight into point set registration, and substantially improves the robustness against outliers. Additionally, we investigate the effect of fuzzier in fuzzy clustering on the cross-modality registration problem, from which we theoretically prove that the classical Iterative Closest Point (ICP) algorithm is a special case of our newly defined objective function. Comprehensive experiments and analysis are conducted on both synthetic and real-world cross-modality datasets. Qualitative and quantitative results demonstrate that our method outperforms state-of-the-art approaches with higher accuracy and robustness. Our code is publicly available at https://github.com/zikai1/CrossModReg .
ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2023.3247169