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).
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work has been partially funded by the Cluster of Excellence "Multi-Modal Computing and Interaction" and the IMPECS collaboration network. The authors wish to thank the anonymous reviewers for their valuable comments and Martin Bokeloh for discussions and his help with the implementation.
ASJC Scopus subject areas
- Computer Networks and Communications