Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras

Can Chen, Haiting Lin, Zhan Yu, Sing Bing Kang, Jingyi Yu

CVPR 2014

doi:10.1109/CVPR.2014.197 /cvpr/2014/chen2014cvpr-light/

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.

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Cite

Text

Chen et al. "Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.197

Markdown

[Chen et al. "Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/chen2014cvpr-light/) doi:10.1109/CVPR.2014.197

BibTeX

@inproceedings{chen2014cvpr-light,
  title     = {{Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras}},
  author    = {Chen, Can and Lin, Haiting and Yu, Zhan and Kang, Sing Bing and Yu, Jingyi},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.197},
  url       = {https://mlanthology.org/cvpr/2014/chen2014cvpr-light/}
}