A Simple Model for Intrinsic Image Decomposition with Depth Cues
Abstract
We present a model for intrinsic decomposition of RGB-D images. Our approach analyzes a single RGB-D image and estimates albedo and shading fields that explain the input. To disambiguate the problem, our model estimates a number of components that jointly account for the reconstructed shading. By decomposing the shading field, we can build in assumptions about image formation that help distinguish reflectance variation from shading. These assumptions are expressed as simple nonlocal regularizers. We evaluate the model on real-world images and on a challenging synthetic dataset. The experimental results demonstrate that the presented approach outperforms prior models for intrinsic decomposition of RGB-D images.
Cite
Text
Chen and Koltun. "A Simple Model for Intrinsic Image Decomposition with Depth Cues." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.37Markdown
[Chen and Koltun. "A Simple Model for Intrinsic Image Decomposition with Depth Cues." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/chen2013iccv-simple/) doi:10.1109/ICCV.2013.37BibTeX
@inproceedings{chen2013iccv-simple,
title = {{A Simple Model for Intrinsic Image Decomposition with Depth Cues}},
author = {Chen, Qifeng and Koltun, Vladlen},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.37},
url = {https://mlanthology.org/iccv/2013/chen2013iccv-simple/}
}