research-article

Intrinsic images in the wild

Authors:

Noah SnavelyAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 33, Issue 4

Article No.: 159, Pages 1 - 12

https://doi.org/10.1145/2601097.2601206

Published: 27 July 2014 Publication History

Abstract

Intrinsic image decomposition separates an image into a reflectance layer and a shading layer. Automatic intrinsic image decomposition remains a significant challenge, particularly for real-world scenes. Advances on this longstanding problem have been spurred by public datasets of ground truth data, such as the MIT Intrinsic Images dataset. However, the difficulty of acquiring ground truth data has meant that such datasets cover a small range of materials and objects. In contrast, real-world scenes contain a rich range of shapes and materials, lit by complex illumination.

In this paper we introduce Intrinsic Images in the Wild, a large-scale, public dataset for evaluating intrinsic image decompositions of indoor scenes. We create this benchmark through millions of crowdsourced annotations of relative comparisons of material properties at pairs of points in each scene. Crowdsourcing enables a scalable approach to acquiring a large database, and uses the ability of humans to judge material comparisons, despite variations in illumination. Given our database, we develop a dense CRF-based intrinsic image algorithm for images in the wild that outperforms a range of state-of-the-art intrinsic image algorithms. Intrinsic image decomposition remains a challenging problem; we release our code and database publicly to support future research on this problem, available online at http://intrinsic.cs.cornell.edu/.

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Cited By

Careaga CAksoy Y(2024)Colorful Diffuse Intrinsic Image Decomposition in the WildACM Transactions on Graphics10.1145/368798443:6(1-12)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687984
Yoon JShu ZRen MZhang CHold-Geoffroy YSingh KZhang H(2024)Generative Portrait Shadow RemovalACM Transactions on Graphics10.1145/368790343:6(1-13)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687903
Luo JCeylan DYoon JZhao NPhilip JFrühstück ALi WRichardt CWang T(2024)IntrinsicDiffusion: Joint Intrinsic Layers from Latent Diffusion ModelsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657472(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657472
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Index Terms

Intrinsic images in the wild
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 33, Issue 4

July 2014

1366 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2601097

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Copyright © 2014 ACM.

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Publication History

Published: 27 July 2014

Published in TOG Volume 33, Issue 4

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Cited By

Careaga CAksoy Y(2024)Colorful Diffuse Intrinsic Image Decomposition in the WildACM Transactions on Graphics10.1145/368798443:6(1-12)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687984
Yoon JShu ZRen MZhang CHold-Geoffroy YSingh KZhang H(2024)Generative Portrait Shadow RemovalACM Transactions on Graphics10.1145/368790343:6(1-13)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687903
Luo JCeylan DYoon JZhao NPhilip JFrühstück ALi WRichardt CWang T(2024)IntrinsicDiffusion: Joint Intrinsic Layers from Latent Diffusion ModelsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657472(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657472
Zeng ZDeschaintre VGeorgiev IHold-Geoffroy YHu YLuan FYan LHašan M(2024)RGB↔X: Image decomposition and synthesis using material- and lighting-aware diffusion modelsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657445(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657445
Xu RZhang LZhang F(2024)Intrinsic Omnidirectional Image Decomposition With Illumination Pre-ExtractionIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336634330:7(4416-4428)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3366343
Zhan YWang TShao TZhou K(2024)Pattern Guided UV Recovery for Realistic Video Garment TexturingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.335472730:12(7531-7543)Online publication date: Dec-2024
https://doi.org/10.1109/TVCG.2024.3354727
Luo JZhao NLi WRichardt C(2024)CRefNet: Learning Consistent Reflectance Estimation With a Decoder-Sharing TransformerIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.333787030:9(6407-6420)Online publication date: Sep-2024
https://doi.org/10.1109/TVCG.2023.3337870
Xie WLiu TGu Y(2024)Intrinsic Hyperspectral Image Recovery for UAV Strips StitchingIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.343302462(1-13)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3433024
Li RDai PLiu GZhang SZeng BLiu S(2024)PBR-GAN: Imitating Physically-Based Rendering With Generative Adversarial NetworksIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329892934:3(1827-1840)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3298929
Schreiber ASivakumar ADu PGasparino MChowdhary GDriggs-Campbell K(2024)W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile RoboticsIEEE Robotics and Automation Letters10.1109/LRA.2024.33960959:6(5623-5630)Online publication date: Jun-2024
https://doi.org/10.1109/LRA.2024.3396095
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