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Discovering Structured Variations Via Template Matching
Author(s) -
Ceylan Duygu,
Dang Minh,
Mitra Niloy J.,
Neubert Boris,
Pauly Mark
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12788
Subject(s) - matching (statistics) , computer science , template , base (topology) , set (abstract data type) , deformation (meteorology) , variation (astronomy) , context (archaeology) , raw data , artificial intelligence , pattern recognition (psychology) , mathematics , statistics , mathematical analysis , paleontology , physics , meteorology , astrophysics , biology , programming language
Understanding patterns of variation from raw measurement data remains a central goal of shape analysis. Such an understanding reveals which elements are repeated, or how elements can be derived as structured variations from a common base element. We investigate this problem in the context of 3D acquisitions of buildings. Utilizing a set of template models, we discover geometric similarities across a set of building elements. Each template is equipped with a deformation model that defines variations of a base geometry. Central to our algorithm is a simultaneous template matching and deformation analysis that detects patterns across building elements by extracting similarities in the deformation modes of their matching templates. We demonstrate that such an analysis can successfully detect structured variations even for noisy and incomplete data.