Joint Estimation of Depth, Reflectance and Illumination for Depth Refinement
Abstract
In this paper we propose a method for joint estimation of depth, reflectance and illumination from a single RGB-D image for depth refinement. This is achieved by a simple optimization based approach with smoothness constraints on depth, reflectance and illumination. We introduce an adaptively weighted local similarity constraint for reflectance, a normalized spherical-harmonic model for illumination, and an edge-aware local smoothness constraint for depth. This allows us to generate high quality depth without additional processes such as pre-training of stochastic models or image segmentation. Experimental results demonstrate that our method estimates high quality depth in comparison with ground-truth data not only for laboratory conditions but also for complex real-world scenes.
Cite
Text
Kim et al. "Joint Estimation of Depth, Reflectance and Illumination for Depth Refinement." IEEE/CVF International Conference on Computer Vision Workshops, 2015. doi:10.1109/ICCVW.2015.35Markdown
[Kim et al. "Joint Estimation of Depth, Reflectance and Illumination for Depth Refinement." IEEE/CVF International Conference on Computer Vision Workshops, 2015.](https://mlanthology.org/iccvw/2015/kim2015iccvw-joint/) doi:10.1109/ICCVW.2015.35BibTeX
@inproceedings{kim2015iccvw-joint,
title = {{Joint Estimation of Depth, Reflectance and Illumination for Depth Refinement}},
author = {Kim, Kichang and Torii, Akihiko and Okutomi, Masatoshi},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2015},
pages = {199-207},
doi = {10.1109/ICCVW.2015.35},
url = {https://mlanthology.org/iccvw/2015/kim2015iccvw-joint/}
}