3D model retrieval based on adaptive views clustering

Authors:
Tarik Filali Ansary;Mohamed Daoudi;Jean-Phillipe Vandeborre
Affiliations:
MIIRE Research Group, (GET / INT / LIFL UMR USTL/CNRS 8022);Laboratoire d'Informatique de Tours (EA 2101), Université François-Rabelais;MIIRE Research Group, (GET / INT / LIFL UMR USTL/CNRS 8022)
Venue:
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

In this paper, we propose a method for 3D model indexing based on 2D views, named AVC (Adaptive Views Clustering). The goal of this method is to provide an optimal selection of 2D views from a 3D model, and a probabilistic Bayesian method for 3D model retrieval from these views. The characteristic views selection algorithm is based on an adaptive clustering algorithm and using statistical model distribution scores to select the optimal number of views. Starting from the fact that all views do not contain the same amount of information, we also introduce a novel Bayesian approach to improve the retrieval. We finally present our results and compare our method to some state of the art 3D retrieval descriptors on the Princeton 3D Shape Benchmark database.