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A Maximum Likelihood Approach for Depth Field Estimation Based on Epipolar Plane Images

机译：基于极地平面图像的深度场估计的最大似然法

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In this paper, a multi-resolution method for depth estimation from dense image arrays is presented. Recent progress in consumer electronics has enabled the development of low cost hand-held plenoptic cameras. In these systems, multiple views of a scene are captured in a single shot by means of a micro-lens array placed on the focal point of the first camera lens, in front of the imaging sensor. These views can be processed jointly to obtain accurate depth maps. In this contribution, to reduce the computational complexity associated to global optimization schemes based on match cost functions, we make a local estimate based on the maximization of the total log-likelihood spatial density aggregated along the epipolar lines corresponding to each view pair. This method includes the local maximum likelihood estimation of the depth field based on epipolar plane images. To face the potential accuracy losses associated to the ambiguity problem that arises in flat surface regions while preserving bandwidth in correspondence of the edges, we adopt a multi-resolution scheme. In practice, the depth map resolution is reduced in regions where maximizing the higher resolution functional is ill-conditioned. The main benefits of the proposed system are in a reduced computational complexity and a high accuracy of the estimated depth. Experimental results show that the proposed scheme represents a good tradeoff among accuracy, robustness, and discontinuities handling.
机译：本文提出了一种从密集图像阵列进行深度估计的多分辨率方法。消费类电子产品的最新进展使低成本手持全光摄像机的开发成为可能。在这些系统中，通过放置在第一传感器镜头的焦点上，位于成像传感器前面的微透镜阵列，以单张照片捕获场景的多个视图。这些视图可以一起处理以获得准确的深度图。在此贡献中，为了降低与基于匹配成本函数的全局优化方案相关的计算复杂性，我们基于沿对应于每个视图对的对极线聚合的总对数似然空间密度的最大值进行局部估计。该方法包括基于对极平面图像的深度场的局部最大似然估计。为了解决与在平面区域中出现的歧义问题相关的潜在精度损失，同时保留对应于边缘的带宽，我们采用了多分辨率方案。实际上，在使高分辨率功能最大化的区域中，深度图分辨率降低了。所提出的系统的主要优点在于减少了计算复杂度和估计深度的高精度。实验结果表明，该方案在精度，鲁棒性和不连续性处理之间表现出良好的折衷。

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