Shape analysis with subspace symmetries

Alexander Berner, Michael D. Wand, Niloy J. Mitra, Daniel Mewes, Hans Peter Seidel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

We address the problem of partial symmetry detection, i.e., the identification of building blocks a complex shape is composed of. Previous techniques identify parts that relate to each other by simple rigid mappings, similarity transforms, or, more recently, intrinsic isometries. Our approach generalizes the notion of partial symmetries to more general deformations. We introduce subspace symmetries whereby we characterize similarity by requiring the set of symmetric parts to form a low dimensional shape space. We present an algorithm to discover subspace symmetries based on detecting linearly correlated correspondences among graphs of invariant features. We evaluate our technique on various data sets. We show that for models with pronounced surface features, subspace symmetries can be found fully automatically. For complicated cases, a small amount of user input is used to resolve ambiguities. Our technique computes dense correspondences that can subsequently be used in various applications, such as model repair and denoising. © 2010 The Author(s).
Original languageEnglish (US)
Title of host publicationComputer Graphics Forum
PublisherWiley
Pages277-286
Number of pages10
DOIs
StatePublished - Apr 28 2011

ASJC Scopus subject areas

  • Computer Networks and Communications

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