Depth prediction from a single image with conditional adversarial networks

Hyungjoo Jung, Youngjung Kim, Dongbo Min, Changjae Oh, Kwanghoon Sohn

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

8 Citations (Scopus)

Abstract

Recent works on machine learning have greatly advanced the accuracy of depth estimation from a single image. However, resulting depth images are still visually unsatisfactory, often producing poor boundary localization and spurious regions. In this paper, we formulate this problem from single images as a deep adversarial learning framework. A two-stage convolutional network is designed as a generator to sequentially predict global and local structures of the depth image. At the heart of our approach is a training criterion based on adversarial discriminator which attempts to distinguish between real and generated depth images as accurately as possible. Our model enables more realistic and structure-preserving depth prediction from a single image, compared to state-of-the-arts approaches. An experimental comparison demonstrates the effectiveness of our approach on large RGB-D dataset.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages1717-1721
Number of pages5
ISBN (Electronic)9781509021758
DOIs
Publication statusPublished - 2018 Feb 20
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 2017 Sep 172017 Sep 20

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
Country/TerritoryChina
CityBeijing
Period17/9/1717/9/20

Bibliographical note

Funding Information:
This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0115-16-1007, High quality 2d-to-multiview contents generation from large-scale RGB+D database).

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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