Similarity Detection for Free-Form Parametric Models
Year: 2013
Type: conference-paper
Abstract: In this article, we propose a framework for detecting local similarities in free-form parametric models, in particular\non B-Splines or NURBS based B-reps: patches similar up to an approximated isometry are identified. Many recent\narticles have tackled similarity detection on 3D objects, in particular on 3D meshes. The parametric B-splines, or\nNURBS models are standard in the CAD (Computer Aided Design) industry, and similarity detection opens the\ndoor to interesting applications in this domain, such as model editing, objects comparison or efficient coding. Our\ncontributions are twofold: we adapt the current technique called votes transformation space for parametric surfaces\nand we improve the identification of isometries. First, an orientation technique independent of the parameterization\npermits to identify direct versus indirect transformations. Second, the validation step is generalized to extend to\nthe whole B-rep. Then, by classifying the isometries according to their fixed points, we simplify the clustering\nstep. We also apply an unsupervised spectral clustering method which improves the results but also automatically\nestimates the number of clusters.
Authors: Dang, Quoc Viet, Mouysset, Sandrine, Morin, Géraldine
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2280875159