Multi-View Stereo (MVS) methods tackle the ill-posed inverse problem of recovering an object's 3D structure from its multi-view calibrated images. High computational cost restricts most MVS methods from using global information for depth estimation. We present a depth map-based MVS method that uses global information to estimate the depths of all pixels in an image simultaneously. To this end, we transform the depth refinement problem into computing max-flow/min-cut on a 3D grid graph with offset vertices. The $s{-}t$ min-cut of this graph corresponds to the minimization of an energy functional consisting of photo-consistency and smoothness terms. Experimental results on indoor and outdoor datasets validate the efficacy of our method, especially on models with low textured regions where global information is necessary to infer the correct depth.

中文翻译：

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使用基于图形切割的深度细化的多视图立体

多视图立体 (MVS) 方法解决了从多视图校准图像中恢复对象的 3D 结构的不适定逆问题。高计算成本限制了大多数 MVS 方法使用全局信息进行深度估计。我们提出了一种基于深度图的 MVS 方法，该方法使用全局信息同时估计图像中所有像素的深度。为此，我们将深度细化问题转换为在具有偏移顶点的 3D 网格图上计算最大流/最小切。这$s{-}t$该图的最小切割对应于由光一致性和平滑项组成的能量泛函的最小化。室内和室外数据集的实验结果验证了我们方法的有效性，特别是在具有低纹理区域的模型上，其中需要全局信息来推断正确的深度。