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A Bayesian Framework for Simultaneous Matting and 3D Reconstruction

机译:贝叶斯同时进行抠图和3D重建的框架

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Conventional approaches to 3D scene reconstruction often treat matting and reconstruction as two separate problems, with matting a prerequisite to reconstruction. The problem with such an approach is that it requires taking irreversible decisions at the first stage, which may translate into reconstruction errors at the second stage. In this paper, we propose an approach which attempts to solve both problems jointly, thereby avoiding this limitation. A general Bayesian formulation for estimating opacity and depth with respect to a reference camera is developed. In addition, it is demonstrated that in the special case of binary opacity values (background/foreground) and discrete depth values, a global solution can be obtained via a single graph-cut computation. We demonstrate the application of the method to novel view synthesis in the case of a large-scale outdoor scene. An experimental comparison with a two-stage approach based on chroma-keying and shape-from-silhouette illustrates the advantages of the new method.
机译:传统的3D场景重建方法通常将遮罩和重建视为两个独立的问题,而遮罩是重建的先决条件。这种方法的问题在于,它需要在第一阶段做出不可逆的决定,这可能会在第二阶段转化为重构错误。在本文中,我们提出了一种试图共同解决这两个问题的方法,从而避免了这种局限性。开发了用于估计相对于参考相机的不透明度和深度的通用贝叶斯公式。另外,证明了在二进制不透明度值(背景/前景)和离散深度值的特殊情况下,可以通过单个图割计算获得全局解。我们演示了该方法在大型室外场景中新颖的视图合成中的应用。与基于色键和轮廓形状的两阶段方法进行的实验比较说明了该方法的优点。

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