Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
An Evolution Perception Mechanism for Organizing and Displaying 3D Shape Collections Read chapter Read first chapter

2017 | OriginalPaper | Chapter

How Depth Estimation in Light Fields Can Benefit from Angular Super-Resolution?

Authors : Mandan Zhao, Gaochang Wu, Yebin Liu, Xiangyang Hao

Published in: Computer Vision

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With the development of consumer light field cameras, the light field imaging has become an extensively used method for capturing the 3D appearance of a scene. The depth estimation often require a dense sampled light field in the angular domain. However, there is an inherent trade-off between the angular and spatial resolution of the light field. Recently, some studies for novel view synthesis or angular super-resolution from a sparse set of have been introduced. Rather than the conventional approaches that optimize the depth maps, these approaches focus on maximizing the quality of synthetic views. In this paper, we investigate how the depth estimation can benefit from these angular super-resolution methods. Specifically, we compare the qualities of the estimated depth using the original sparse sampled light fields and the reconstructed dense sampled light fields. Experiment results evaluate the enhanced depth maps using different view synthesis approaches.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Multi-context Deep Convolutional Features and Exemplar-SVMs for Scene Parsing
next chapter Influence Evaluation for Image Tampering Using Saliency Mechanism
Literature
1.
2.
3.
4.
5.
Kalantari, N.K., Wang, T.C., Ramamoorthi, R.: Learning-based view synthesis for light field cameras. ACM Trans. Graph. (TOG) 35(6), 193–202 (2016)CrossRef
6.
7.
Pujades, S., Devernay, F., Goldluecke, B.: Bayesian view synthesis and image-based rendering principles. In: CVPR, pp. 3906–3913 (2014)
8.
Shi, L., Hassanieh, H., Davis, A., Katabi, D., Durand, F.: Light field reconstruction using sparsity in the continuous fourier domain. ACM TOG 34(1), 12 (2014)CrossRefMATH
9.
Tao, M.W., Hadap, S., Malik, J., Ramamoorthi, R.: Depth from combining defocus and correspondence using light-field cameras. In: ICCV, pp. 673–680 (2013)
10.
Vagharshakyan, S., Bregovic, R., Gotchev, A.: Image based rendering technique via sparse representation in shearlet domain. In: ICIP, pp. 1379–1383. IEEE (2015)
11.
Wang, T.C., Efros, A.A., Ramamoorthi, R.: Occlusion-aware depth estimation using light-field cameras. In: ICCV, pp. 3487–3495 (2015)
12.
Wanner, S., Goldluecke, B.: Variational light field analysis for disparity estimation and super-resolution. IEEE TPAMI 36(3), 606–619 (2014)CrossRef
13.
Wanner, S., Meister, S., Goldlücke, B.: Datasets and benchmarks for densely sampled 4D light fields. In: Vision, Modeling & Visualization, pp. 225–226 (2013)
14.
Wu, G., Zhao, M., Wang, L., Chai, T., Dai, Q., Liu, Y.: Light field reconstruction using deep convolutional network on EPI. In: CVPR (2017)
15.
Yoon, Y., Jeon, H.G., Yoo, D., Lee, J.Y., So Kweon, I.: Learning a deep convolutional network for light-field image super-resolution. In: CVPRW, pp. 24–32 (2015)
16.
Zhang, Z., Liu, Y., Dai, Q.: Light field from micro-baseline image pair. In: CVPR, pp. 3800–3809 (2015)
Metadata
Title
How Depth Estimation in Light Fields Can Benefit from Angular Super-Resolution?
Authors
Mandan Zhao
Gaochang Wu
Yebin Liu
Xiangyang Hao
Copyright Year
2017
Publisher
Springer Singapore
Book
Computer Vision

Print ISBN: 978-981-10-7298-7

Electronic ISBN: 978-981-10-7299-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7299-4_42

Premium Partner

    Image Credits