Functional map networks for analyzing and exploring large shape collections
Abstract
The construction of networks of maps among shapes in a collection enables a variety of applications in data-driven geometry processing. A key task in network construction is to make the maps consistent with each other. This consistency constraint, when properly defined, leads not only to a concise representation of such networks, but more importantly, it serves as a strong regularizer for correcting and improving noisy initial maps computed between pairs of shapes in isolation. Up-to-now, however, the consistency constraint has only been fully formulated for point-based maps or for shape collections that are fully similar.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 27, 2014
- Source ID
- 10.1145/2601097.2601111
Entities
People
- Fan Wang
- Leonidas J. Guibas
- Qixing Huang
Organizations
- Air Force Office of Scientific Research
- Division of Computer and Network Systems
- Division of Computing and Communication Foundations
- National Science Foundation Division of Mathematical Sciences
- Stanford University