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MRF Labeling for Multi-view Range Image Integration

Song, Ran, Liu, Yonghuai, Martin, Ralph Robert and Rosin, Paul L. 2011. MRF Labeling for Multi-view Range Image Integration. Lecture Notes in Computer Science 6493 , pp. 27-40. 10.1007/978-3-642-19309-5_3

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Abstract

Multi-view range image integration focuses on producing a single reasonable 3D point cloud from multiple 2.5D range images for the reconstruction of a watertight manifold surface. However, registration errors and scanning noise usually lead to a poor integration and, as a result, the reconstructed surface cannot have topology and geometry consistent with the data source. This paper proposes a novel method cast in the framework of Markov random fields (MRF) to address the problem. We define a probabilistic description of a MRF labeling based on all input range images and then employ loopy belief propagation to solve this MRF, leading to a globally optimised integration with accurate local details. Experiments show the advantages and superiority of our MRF-based approach over existing methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information: PDF uploaded in accordance with publisher's policy http://www.springer.com/gp/open-access/authors-rights/self-archiving-policy/2124 [accessed 14/04/2015] The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-19309-5_3
Publisher: Springer Verlag
ISSN: 0302-9743
Last Modified: 05 Jun 2017 08:52
URI: http://orca-mwe.cf.ac.uk/id/eprint/11421

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