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báo cáo hóa học:" Research Article From 2D Silhouettes to 3D Object Retrieval: Contributions and Benchmarking"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article From 2D Silhouettes to 3D Object Retrieval: Contributions and Benchmarking | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2010 Article ID 367181 17 pages doi 10.1155 2010 367181 Research Article From 2D Silhouettes to 3D Object Retrieval Contributions and Benchmarking Thibault Napoleon and Hichem Sahbi Telecom ParisTech CNRS LTCI UMR 5141 46 rue Barrault 75013 Paris France Correspondence should be addressed to Thibault Napoleon thibault.napoleon@telecom-paristech.fr Received 3 August 2009 Revised 2 December 2009 Accepted 2 March 2010 Academic Editor Dietmar Saupe Copyright 2010 T. Napoleon and H. Sahbi. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 3D retrieval has recently emerged as an important boost for 2D search techniques. This is mainly due to its several complementary aspects for instance enriching views in 2D image datasets overcoming occlusion and serving in many real-world applications such as photography art archeology and geolocalization. In this paper we introduce a complete 2D photography to 3D object retrieval framework. Given a collection of picture s or sketch es of the same scene or object the method allows us to retrieve the underlying similar objects in a database of 3D models. The contribution of our method includes i a generative approach for alignment able to find canonical views consistently through scenes objects and ii the application of an efficient but effective matching method used for ranking. The results are reported through the Princeton Shape Benchmark and the Shrec benchmarking consortium evaluated compared by a third party. In the two gallery sets our framework achieves very encouraging performance and outperforms the other runs. 1. Introduction 3D object recognition and retrieval recently gained a big interest 27 because of the limitation of the 2D-to-2D approaches. The latter suffer from several .