Boyer, E.Bronstein, A. M.Bronstein, M. M.Bustos, B.Darom, T.Horaud, R.Hotz, I.Keller, Y.Keustermans, J.Kovnatsky, A.Litmany, R.Reininghaus, J.Sipiran, I.Smeets, D.Suetens, P.Vandermeulen, D.Zaharescu, A.Zobel, V.H. Laga and T. Schreck and A. Ferreira and A. Godil and I. Pratikakis and R. Veltkamp2013-04-252013-04-252011978-3-905674-31-61997-0463https://doi.org/10.2312/3DOR/3DOR11/071-078Feature-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 resultsCategories and Subject Descriptors (according to ACM CCS): H.3.2 [Information storage and retrieval]: Information Search and Retrieval-Retrieval models I.2.10 [Artificial intelligence]: Vision and Scene Understanding-Shape