3D Plant Growth Prediction via Image-to-Image Translation

Tomohiro Hamamoto, Hideaki Uchiyama, Atsushi Shimada, Rin-ichiro Taniguchi

2020

Abstract

This paper presents a method to predict three-dimensional (3D) plant growth with RGB-D images. Based on neural network based image translation and time-series prediction, we construct a system that gives the predicted result of RGB-D images from several past RGB-D images. Since both RGB and depth images are incorporated into our system, the plant growth can be represented in 3D space. In the evaluation, the performance of our proposed network is investigated by focusing on clarifying the importance of each module in the network. We have verified how the prediction accuracy changes depending on the internal structure of the our network.

Paper Citation

in Harvard Style

Hamamoto T., Uchiyama H., Shimada A. and Taniguchi R. (2020). 3D Plant Growth Prediction via Image-to-Image Translation. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 153-161. DOI: 10.5220/0008989201530161

in Bibtex Style

@conference{visapp20,
author={Tomohiro Hamamoto and Hideaki Uchiyama and Atsushi Shimada and Rin-ichiro Taniguchi},
title={3D Plant Growth Prediction via Image-to-Image Translation},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={153-161},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008989201530161},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - 3D Plant Growth Prediction via Image-to-Image Translation
SN - 978-989-758-402-2
AU - Hamamoto T.
AU - Uchiyama H.
AU - Shimada A.
AU - Taniguchi R.
PY - 2020
SP - 153
EP - 161
DO - 10.5220/0008989201530161
PB - SciTePress