A Bayesian 3-D search engine using adaptive views clustering

In this paper, we propose a method for three-dimensional (3-D)-model indexing based on two-dimensional (2-D) views, which we call adaptive views clustering (AVC). The goal of this method is to provide an optimal selection of 2-D views from a 3-D model, and a probabilistic Bayesian method for 3-D-model retrieval from these views. The characteristic view selection algorithm is based on an adaptive clustering algorithm and uses statistical model distribution scores to select the optimal number of views. Starting from the fact that all views do not have equal importance, we also introduce a novel Bayesian approach to improve the retrieval. Finally, we present our results and compare our method to some state-of-the-art 3-D retrieval descriptors on the Princeton 3-D Shape Benchmark database and a 3-D-CAD-models database supplied by the car manufacturer Renault.