Single Image Multi-Spectral Photometric Stereo Using a Split U-Shaped CNN
Abstract
We present a system to extract surface orientation and albedos from a single shot image using three differently colored illumination sources. Photometric stereo allows one to extract local surface information such as normals or gradients. Traditionally, the local orientations and albedos are computed using serveral acquisitions of the same viewing angle and under varying illumination directions. In applications with moving objects, where the acquisition-as well as processing speed are essential, such setups are poorly suited. We propose a single shot decomposition using three differently colored light sources under defined illumination directions. To allow for a fast and regularized inference, we built a split U-shaped convolutional neural network, which takes a single shot input and estimates both the surface orientation and albedo simultaneously.
Cite
Text
Antensteiner et al. "Single Image Multi-Spectral Photometric Stereo Using a Split U-Shaped CNN." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00065Markdown
[Antensteiner et al. "Single Image Multi-Spectral Photometric Stereo Using a Split U-Shaped CNN." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/antensteiner2019cvprw-single/) doi:10.1109/CVPRW.2019.00065BibTeX
@inproceedings{antensteiner2019cvprw-single,
title = {{Single Image Multi-Spectral Photometric Stereo Using a Split U-Shaped CNN}},
author = {Antensteiner, Doris and Stolc, Svorad and Soukup, Daniel},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {481-483},
doi = {10.1109/CVPRW.2019.00065},
url = {https://mlanthology.org/cvprw/2019/antensteiner2019cvprw-single/}
}