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Distinctive Image Features from Scale-Invariant Keypoints
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ACM SIGGRAPH 2004 Papers
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Image Matching Using Photometric Information
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Multi-scale features for approximate alignment of point-based surfaces
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
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ACM SIGGRAPH Asia 2008 papers
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ACM SIGGRAPH Asia 2009 papers
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International Journal of Computer Vision
Contextual Part Analogies in 3D Objects
International Journal of Computer Vision
SMI 2011: Full Paper: Geometric models with weigthed topology
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Editorial: Special Section on Semantic 3D Media and Content
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Field-guided registration for feature-conforming shape composition
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
A Machine-Learning Approach to Keypoint Detection and Landmarking on 3D Meshes
International Journal of Computer Vision
3D partial surface matching using differential geometry and statistical approaches
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Recognising 3D products and sourcing part documentation with scanned data
Computers in Industry
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Partial matching is a fundamental problem in shape analysis, a field that is recently gaining an increasing importance in computer graphics. This paper proposes a novel approach to performing partial matching of surfaces. Given two surfaces M"A and M"B, our goal is to find the best match to M"A within M"B. The key idea of our approach is to integrate feature-point similarity and segment similarity. Specifically, we introduce a probabilistic framework in which the segmentation and the correspondences of neighboring feature points allow us to enhance or moderate our certainty of a feature-point similarity. The utility of our algorithm is demonstrated in the domain of archaeology, where digital archiving is becoming ever more widespread. In this domain, automatic matching can serve as a worthy alternative to the expensive and time-consuming manual procedure that is used today.