Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras

Can Chen, Haiting Lin, Zhan Yu, Sing Bing Kang, Jingyi Yu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1518-1525

Abstract


In this paper, we introduce a bilateral consistency metric on the surface camera (SCam) for light field stereo matching to handle significant occlusions. The concept of SCam is used to model angular radiance distribution with respect to a 3D point. Our bilateral consistency metric is used to indicate the probability of occlusions by analyzing the SCams. We further show how to distinguish between on-surface and free space, textured and non-textured, and Lambertian and specular through bilateral SCam analysis. To speed up the matching process, we apply the edge preserving guided filter on the consistency-disparity curves. Experimental results show that our technique outperforms both the state-of-the-art and the recent light field stereo matching methods, especially near occlusion boundaries.

Related Material


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[bibtex]
@InProceedings{Chen_2014_CVPR,
author = {Chen, Can and Lin, Haiting and Yu, Zhan and Bing Kang, Sing and Yu, Jingyi},
title = {Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}