Joint inpainting of RGB and depth images by generative adversarial network with a late fusion approach

Ryo Fujii, Ryo Hachiuma, Hideo Saito

研究成果: Conference contribution

抜粋

Image inpainting aims to restore texture of missing regions in scene from an RGB image. In this paper, we aim to restore not only the texture but also the geometry of the missing regions in scene from a pair of RGB and depth images. Inspired by the recent development of generative adversarial network, we employ an encoder-decoderbased generative adversarial network with the input of RGB and depth image. The experimental results show that our method restores the missing region of both RGB and depth image.

元の言語English
ホスト出版物のタイトルAdjunct Proceedings of the 2019 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2019
出版者Institute of Electrical and Electronics Engineers Inc.
ページ203-204
ページ数2
ISBN(電子版)9781728147659
DOI
出版物ステータスPublished - 2019 10
イベント18th IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2019 - Beijing, China
継続期間: 2019 10 142019 10 18

出版物シリーズ

名前Adjunct Proceedings of the 2019 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2019

Conference

Conference18th IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2019
China
Beijing
期間19/10/1419/10/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Media Technology

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  • これを引用

    Fujii, R., Hachiuma, R., & Saito, H. (2019). Joint inpainting of RGB and depth images by generative adversarial network with a late fusion approach. : Adjunct Proceedings of the 2019 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2019 (pp. 203-204). [8951904] (Adjunct Proceedings of the 2019 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISMAR-Adjunct.2019.00-46