SHREC 2011: robust feature detection and description benchmark

Authors:
E. Boyer;A. M. Bronstein;M. M. Bronstein;B. Bustos;T. Darom;R. Horaud;I. Hotz;Y. Keller;J. Keustermans;A. Kovnatsky;R. Litman;J. Reininghaus;I. Sipiran;D. Smeets;P. Suetens;D. Vandermeulen;A. Zaharescu;V. Zobel
Affiliations:
INRIA Grenoble, Rhône-Alpes, France;Department of Electrical Engineering, Tel Aviv University, Israel;Institute of Computational Science, Faculty of Informatics, Università della Svizzera Italiana, Lugano, Switzerland;Department of Computer Science, University of Chile;School of Engineering, Bar-Ilan University, Ramat-Gan, Israel;INRIA Grenoble, Rhône-Alpes, France;Zuse Institut Berlin, Germany;School of Engineering, Bar-Ilan University, Ramat-Gan, Israel;Department of Electrical Engineering, K.U. Leuven, Belgium;Department of Mathematics, Technion-Israel Institute of Technology, Haifa, Israel;Department of Electrical Engineering, Tel Aviv University, Israel;Zuse Institut Berlin, Germany;Department of Computer Science, University of Chile;Department of Electrical Engineering, K.U. Leuven, Belgium;Department of Electrical Engineering, K.U. Leuven, Belgium;Department of Electrical Engineering, K.U. Leuven, Belgium;Aimetis Corp., Waterloo, Canada;Zuse Institut Berlin, Germany
Venue:
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
Year:
2011

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Abstract

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