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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Song, R., Liu, Y., Martin, R. R., & Rosin, P. L. (2011). MRF labeling for multi-view range image integration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6493 LNCS, pp. 27–40). https://doi.org/10.1007/978-3-642-19309-5_3
Mendeley helps you to discover research relevant for your work.