Light-field stereo matching problems are commonly modeled by Markov Random Fields (MRFs) for statistical inference of depth maps. Nevertheless, most previous approaches did not adapt to image statistics but instead adopted fixed model parameters. They explored explicit vision cues, such as depth consistency and occlusion, to provide local adaptability and enhance depth quality. However, such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense view sampling are not suitable for sparse one. In this paper, we get back to MRF fundamentals and develop an empirical Bayesian framework—Robust Pseudo Random Field—to explore intrinsic statistical cues for broad applicability. Based on pseudo-likelihoods with hidden soft-decision priors, we apply soft expectation-maximization (EM) for good model fitting and perform hard EM for robust depth estimation. We introduce novel pixel difference models to enable such adaptability and robustness simultaneously. Accordingly, we devise a stereo matching algorithm to employ this framework on dense, sparse, and even denoised light fields. It can be applied to both true-color and grey-scale pixels. Experimental results show that it estimates scene-dependent parameters robustly and converges quickly. In terms of depth accuracy and computation speed, it also outperforms state-of-the-art algorithms constantly.

中文翻译：

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鲁棒伪随机场建模的经验贝叶斯光场立体匹配

通常用马尔可夫随机场（MRF）建模光场立体匹配问题，以进行深度图的统计推断。尽管如此，大多数以前的方法都不适合图像统计，而是采用了固定的模型参数。他们探索了明确的视觉提示，例如深度一致性和遮挡，以提供局部适应性并提高深度质量。但是，这样的附加假设可能最终会限制其适用性，例如，为密集视图采样而设计的算法不适合稀疏算法。在本文中，我们将回到MRF基础上，并开发一个经验贝叶斯框架（鲁棒伪随机域），以探索内在的统计线索以提供广泛的适用性。基于具有隐藏的软决策先验的伪似然，我们应用软期望最大化（EM）进行良好的模型拟合，并应用硬EM进行稳健的深度估计。我们引入新颖的像素差异模型，以同时实现这种适应性和鲁棒性。因此，我们设计了一种立体匹配算法，以在密集，稀疏，甚至去噪的光场上采用此框架。它可以应用于真彩色和灰度像素。实验结果表明，该算法可以鲁棒地估计场景相关参数，并且可以快速收敛。在深度精度和计算速度方面，它也始终优于最新的算法。甚至是去噪的光场 它可以应用于真彩色和灰度像素。实验结果表明，该算法可以鲁棒地估计场景相关参数，并且可以快速收敛。在深度精度和计算速度方面，它也始终优于最新的算法。甚至是去噪的光场 它可以应用于真彩色和灰度像素。实验结果表明，该算法可以鲁棒地估计场景相关参数，并且可以快速收敛。在深度精度和计算速度方面，它也始终优于最新的算法。