Many algorithms on meshes require the minimization of composite objectives, i.e., energies that are compositions of simpler parts. Canonical examples include mesh parameterization and deformation. We propose a second order optimization approach that exploits this composite structure to efficiently converge to a local minimum. Our main observation is that a convex-concave decomposition of the energy constituents is simple and readily available in many cases of practical relevance in graphics. We utilize such convex-concave decompositions to define a tight convex majorizer of the energy, which we employ as a convex second order approximation of the objective function. In contrast to existing approaches that largely use only local convexification, our method is able to take advantage of a more global view on the energy landscape. Our experiments on triangular meshes demonstrate that our approach outperforms the state of the art on standard problems in geometry processing, and potentially provide a unified framework for developing efficient geometric optimization algorithms.
Bibliographical noteFunding Information:
This research was supported in part by the European Research Council starting grants Surf-Comp (Grant No. 307754) and iModel (Grant No. 306877), I-CORE program of the Israel PBC and ISF (Grant No. 4/11) and the Simons Foundation Math+X Investigator award. The authors would like to thank Olga Diamanti, Michael Rabinovich and Ashish Myles for sharing their code; Amir Porat for producing the supplemental video; and the anonymous reviewers for their helpful comments and suggestions.
© 2017 Copyright held by the owner/author(s).
- Geometry processing
- Second order optimization
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design