Robust depth estimation for light field via spinning parallelogram operator

By Shuo Zhang, Hao Sheng, C. Li, Jun Zhang, Zhang Xiong. Computer Vision and Image Understanding, 2016

342 citations recorded by the source corpus.

We propose a spinning parallelogram operator (SPO) for depth estimation on EPI.We apply the SPO to both synthetic and real light field camera images.The SPO is proved to be insensitive to occlusion, noise, spatial aliasing.The SPO has no requirement for depth range and angular resolution. Removing the influence of occlusion on the depth estimation for light field images has always been a difficult problem, especially for highly noisy and aliased images captured by plenoptic cameras. In this paper, a spinning parallelogram operator (SPO) is integrated into a depth estimation framework to solve these problems. Utilizing the regions divided by the operator in an Epipolar Plane Image (EPI), the lines that indicate depth information are located by maximizing the distribution distances of the regions. Unlike traditional multi-view stereo matching methods, the distance measure is able to keep the correct depth information even if they are occluded or noisy. We further choose the relative reliable information among the rich structures in the light field to reduce the influences of occlusion and ambiguity. The discrete labeling problem is then solved by a filter-based algorithm to fast approximate the optimal solution. The major advantage of the proposed method is that it is insensitive to occlusion, noise, and aliasing, and has no requirement for depth range and angular resolution. It therefore can be used in various light field images, especially in plenoptic camera images. Experimental results demonstrate that the proposed method outperforms state-of-the-art depth estimation methods on light field images, including both real world images and synthetic images, especially near occlusion boundaries.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bdf78d39769d8e0308b805d12ab128102613aba4

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