SHREC '11: Robust Feature Detection and Description Benchmark
Edmond Boyer, Alexander M. Bronstein, Michael M. Bronstein, Benjamín Bustos, T. Darom, Radu Horaud, Ingrid Hotz, Yosi Keller, Johannes Keustermans, Artiom Kovnatsky, Roee Litman, Jan Reininghaus, Ivan Sipiran, Dirk Smeets, Paul L. Suetens, Dirk Vandermeulen, Andrei Zaharescu, Valentin Zobel · arXiv (Cornell University) · 2011
Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and strength of the transformations that can be dealt with. The present paper is a report of the SHREC'11 robust feature detection and description benchmark results