3D plant growth prediction via image-to-image translation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationVISAPP
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz
PublisherSciTePress
Pages153-161
Number of pages9
ISBN (Electronic)9789897584022
Publication statusPublished - Jan 1 2020
Event15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020 - Valletta, Malta
Duration: Feb 27 2020Feb 29 2020

Publication series

NameVISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume5

Conference

Conference15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020
CountryMalta
CityValletta
Period2/27/202/29/20

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Hamamoto, T., Uchiyama, H., Shimada, A., & Taniguchi, R. I. (2020). 3D plant growth prediction via image-to-image translation. In G. M. Farinella, P. Radeva, & J. Braz (Eds.), VISAPP (pp. 153-161). (VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications; Vol. 5). SciTePress.