25 January 2011 Multi-view stereo reconstruction via voxel clustering and optimization of parallel volumetric graph cuts
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Traditional multi-view stereo reconstruction via volumetric graph cuts formulates the 3D reconstruction problem as a computationally tractable global optimization using graph cuts. It benefits from a volumetric scene representation and discrete photo consistency is defined on the edge cost with a weighted graph. As the independence between each discrete voxel, it is natural to do the parallel processing with multi-core CPUs or GPU, but after the photo consistency has been estimated, it still need to design a parallel optimized methods to get the optimized labeling results for each voxel. In our paper, we use the parallel volumetric graph cuts methods to solve the above problems. Our algorithm has two main steps, clustering step and parallel graph cuts optimization step. We also introduce an approach for enhancing accuracy and speeding up existing Multi-view 3D reconstruction methods, which based on volumetric graph cuts. The main idea is to decompose the collected photos into some overlapping sets, while the voxels are also be clustered. The voxels consistency estimating and surface labeling with graph cuts are processed in parallel, however, the labels of the overlapped voxels may in general have multiple label solutions. It will be constrained to be equal to obtain a unique solution in parallel graph cuts optimization step.
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Yun-Feng Zhu, Yun-Feng Zhu, Yu-Jin Zhang, Yu-Jin Zhang, } "Multi-view stereo reconstruction via voxel clustering and optimization of parallel volumetric graph cuts", Proc. SPIE 7872, Parallel Processing for Imaging Applications, 78720S (25 January 2011); doi: 10.1117/12.872185; https://doi.org/10.1117/12.872185


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