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Robust multi-view stereo synthesized by various parameters model

机译：通过各种参数模型合成的稳健多视点立体声

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摘要

In this paper, we have developed a novel and robust framework of combining a matrix splitting with multi-view stereo reconstructions to separate reconstruction inaccuracies from a various parameters model for high-accuracy multi-view stereo reconstruction. Instead of performing the fixed parameters reconstruction procedure, we apply the variational based 3D reconstruction algorithm multi-times with various parameters to derive a set of hypothetic 3D models, and then synthesized the final result by formulating the problem as a low-rank matrix splitting problem. Benefited from the matrix splitting formulation, the outliers and bad matches, which are treated as the noise in the synthesized model, are effectively removed and thus lead to a 3D reconstruction with higher accuracy than the existing fixed parameters reconstructions. Constrained convex optimization is introduced for matrix splitting with an accelerated proximal gradient (APG) algorithm integrated for fast convergence. Both the experiments on the Middlebury and real-world data sets have demonstrated the effectiveness of the proposed method. (C) 2016 Published by Elsevier Inc.
机译：在本文中，我们开发了一种新颖而强大的框架，该框架将矩阵拆分与多视图立体重建相结合，以将重建误差与用于高精度多视图立体重建的各种参数模型分开。代替执行固定参数的重建过程，我们将具有各种参数的基于变分的3D重建算法多次应用，以导出一组假设的3D模型，然后通过将问题表示为低秩矩阵分裂问题来合成最终结果。得益于矩阵拆分公式，可以将异常值和不匹配项（在合成模型中被视为噪声）有效地去除，从而导致3D重建比现有固定参数重建具有更高的精度。引入了约束凸优化算法，并结合了用于快速收敛的加速近端梯度（APG）算法进行矩阵拆分。在Middlebury数据集和实际数据集上的实验都证明了该方法的有效性。 （C）2016由Elsevier Inc.发布

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