Color-Light Multi Cascade Network for Single Image Depth Prediction on One Perspective Artifact Images

Aufaclav Zatu Kusuma Frisky, Aufaclav Zatu Kusuma Frisky, Simon Brenner, Sebastian Zambanini, Robert Sablatnig

2022

Abstract

Different color material and extreme lighting change pose a problem for single image depth prediction on archeological artifacts. These conditions can lead to misprediction on the surface of the foreground depth reconstruction. We propose a new method, the Color-Light Multi-Cascade Network, to overcome single image depth prediction limitations under these influences. Two feature extractions based on Multi-Cascade Networks (MCNet) are trained to deal with light and color problems individually for this new approach. By concatenating both of the features, we create a new architecture capable of reducing both color and light problems. Three datasets are used to evaluate the method with respect to color and lighting variations. Our experiments show that the individual Color-MCNet can improve the performance in the presence of color variations and fails to handle extreme light changes; the Light-MCNet, on the other hand, shows consistent results under changing lighting conditions but lacks detail. When joining the feature maps of Color-MCNet and Light-MCNet, we obtain a detailed surface both in the presence of different material colors in relief images, and under different lighting conditions. These results prove that our networks outperform state-of-the-art in limited number dataset. Finally, we also evaluate our joined network on the NYU Depth V2 Dataset to compare it with other state-of-the-art methods and obtain comparable performance.

Paper Citation

in Harvard Style

Frisky A., Brenner S., Zambanini S. and Sablatnig R. (2022). Color-Light Multi Cascade Network for Single Image Depth Prediction on One Perspective Artifact Images. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 909-916. DOI: 10.5220/0010801100003124

in Bibtex Style

@conference{visapp22,
author={Aufaclav Zatu Kusuma Frisky and Simon Brenner and Sebastian Zambanini and Robert Sablatnig},
title={Color-Light Multi Cascade Network for Single Image Depth Prediction on One Perspective Artifact Images},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={909-916},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010801100003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Color-Light Multi Cascade Network for Single Image Depth Prediction on One Perspective Artifact Images
SN - 978-989-758-555-5
AU - Frisky A.
AU - Brenner S.
AU - Zambanini S.
AU - Sablatnig R.
PY - 2022
SP - 909
EP - 916
DO - 10.5220/0010801100003124
PB - SciTePress