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

研究成果: 著書/レポートタイプへの貢献会議での発言

抜粋

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.

元の言語英語
ホスト出版物のタイトルVISAPP
編集者Giovanni Maria Farinella, Petia Radeva, Jose Braz
出版者SciTePress
ページ153-161
ページ数9
ISBN(電子版)9789897584022
出版物ステータス出版済み - 1 1 2020
イベント15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020 - Valletta, マルタ
継続期間: 2 27 20202 29 2020

出版物シリーズ

名前VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
5

会議

会議15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020
マルタ
Valletta
期間2/27/202/29/20

All Science Journal Classification (ASJC) codes

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

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  • これを引用

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