A Bayesian 3-D Search Engine Using Adaptive Views Clustering
Top Cited Papers
- 19 December 2006
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Multimedia
- Vol. 9 (1), 78-88
- https://doi.org/10.1109/tmm.2006.886359
Abstract
In this paper, we propose a method for three-dimensional (3D)-model indexing based on two-dimensional (2D) views, which we call adaptive views clustering (AVC). 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 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 3D retrieval descriptors on the Princeton 3D Shape Benchmark database and a 3D-CAD-models database supplied by the car manufacturer RenaultKeywords
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