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Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields

  • Seungryong Kim
  • Kihong Park
  • Kwanghoon Sohn
  • Stephen Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9912)

Abstract

We present a method for jointly predicting a depth map and intrinsic images from single-image input. The two tasks are formulated in a synergistic manner through a joint conditional random field (CRF) that is solved using a novel convolutional neural network (CNN) architecture, called the joint convolutional neural field (JCNF) model. Tailored to our joint estimation problem, JCNF differs from previous CNNs in its sharing of convolutional activations and layers between networks for each task, its inference in the gradient domain where there exists greater correlation between depth and intrinsic images, and the incorporation of a gradient scale network that learns the confidence of estimated gradients in order to effectively balance them in the solution. This approach is shown to surpass state-of-the-art methods both on single-image depth estimation and on intrinsic image decomposition.

Keywords

Single-image depth estimation Intrinsic image decomposition Conditional random field Convolutional neural networks 

Notes

Acknowledgement

This research was supported by the MSIP (The Ministry of Science, ICT and Future Planning), Korea and Microsoft Research, under ICT/SW Creative research program supervised by the IITP(Institute for Information & Communications Technology Promotion) (IITP-2015-R2212-15-0008).

Supplementary material

419983_1_En_9_MOESM1_ESM.pdf (87.7 mb)
Supplementary material 1 (pdf 89826 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Seungryong Kim
    • 1
  • Kihong Park
    • 1
  • Kwanghoon Sohn
    • 1
  • Stephen Lin
    • 2
  1. 1.Yonsei UniversitySeoulSouth Korea
  2. 2.Microsoft ResearchRedmondUSA

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