A comparison of methods for non-rigid 3D shape retrieval

  • Authors:

  • Zhouhui Lian;Afzal Godil;Benjamin Bustos;Mohamed Daoudi;Jeroen Hermans;Shun Kawamura;Yukinori Kurita;Guillaume Lavoué;Hien Van Nguyen;Ryutarou Ohbuchi;Yuki Ohkita;Yuya Ohishi;Fatih Porikli;Martin Reuter;Ivan Sipiran;Dirk Smeets;Paul Suetens;Hedi Tabia;Dirk Vandermeulen

  • Affiliations:

  • Institute of Computer Science and Technology, Peking University, Beijing 100871, China and National Institute of Standards and Technology, Gaithersburg, USA;National Institute of Standards and Technology, Gaithersburg, USA;Department of Computer Science, University of Chile, Chile;Institut TELECOM, France;Katholieke Universiteit Leuven, Belgium;University of Yamanashi, Japan;University of Yamanashi, Japan;Université de Lyon, CNRS, France;University of Maryland, College Park, USA;University of Yamanashi, Japan;University of Yamanashi, Japan;University of Yamanashi, Japan;Mitsubishi Electric Research Laboratories, Cambridge, USA;Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical/MIT, USA;Department of Computer Science, University of Chile, Chile;Katholieke Universiteit Leuven, Belgium;Katholieke Universiteit Leuven, Belgium;University Lille 1, France;Katholieke Universiteit Leuven, Belgium

  • Venue:
  • Pattern Recognition
  • Year:
  • 2013

Quantified Score

Hi-index 0.01

Visualization

Abstract

Non-rigid 3D shape retrieval has become an active and important research topic in content-based 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using six commonly utilized measures. Models and evaluation tools of the new benchmark are publicly available on our web site [1].