000200856 001__ 200856
000200856 005__ 20190117220137.0
000200856 037__ $$aREP_WORK
000200856 245__ $$aDiscovering Structured Variations via Coupled Template Matching
000200856 269__ $$a2014
000200856 260__ $$c2014
000200856 336__ $$aReports
000200856 520__ $$aUnderstanding patterns of variation from raw measurement data remains a central goal of shape analysis. This understanding reveals which elements are exactly repeated, or how elements can be derived as structured variations from a common base element. We investigate this problem in the context of multi-view stereo reconstruction of buildings. Utilizing a set of template models, we establish geometric and semantic relationships among structural elements found in the data. Each template is equipped with a deformation model that defines correlated variations of a base geometry. Central to our algorithm is a coupled template matching and deformation analysis. This analysis simultaneously detects patterns across building elements by extracting similarities in the deformation modes of their matching templates. We evaluate our algorithm on several challenging datasets and demonstrate that such a coupled analysis can successfully detect structured variations even for noisy and incomplete data
000200856 6531_ $$asymmetry detection
000200856 6531_ $$adeformation
000200856 6531_ $$ashape analysis
000200856 6531_ $$atemplate fitting
000200856 700__ $$0244289$$g190804$$aCeylan, Duygu
000200856 700__ $$0245358$$g199221$$aDang, Ngoc Minh
000200856 700__ $$aMitra, Niloy J.
000200856 700__ $$0246529$$g229269$$aNeubert, Boris
000200856 700__ $$g196500$$aPauly, Mark$$0244286
000200856 909C0 $$xU12168$$0252282$$pLGG
000200856 909CO $$pIC$$preport$$ooai:infoscience.tind.io:200856
000200856 917Z8 $$x190804
000200856 937__ $$aEPFL-REPORT-200856
000200856 973__ $$aEPFL
000200856 980__ $$aREPORT