The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Shape, Illumination, and Reflectance from Shading
Abstract
A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison-there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the most likely explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.
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Cites
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Image denoising using scale mixtures of gaussians in the wavelet domain
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Photometric Method For Determining Surface Orientation From Multiple Images
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Cited by
Eigen, D., & Fergus, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.304 · Cited by 2,862
NeRD: Neural Reflectance Decomposition from Image Collections
Boss, M., Braun, R., Jampani, V., Barron, J. T., Liu, C., & Lensch, H. P. A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01245 · Cited by 438
Self-citationHypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
Roberts, M., Ramapuram, J., Ranjan, A., Kumar, A., Bautista, M. A., Paczan, N., Webb, R., & Susskind, J. M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01073 · Cited by 358
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00768 · Cited by 340
State of the Art on 3D Reconstruction with RGB‐D Cameras
Zollhöfer, M., Stotko, P., Görlitz, A., Theobalt, C., Nießner, M., Klein, R., & Kolb, A. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13386 · Cited by 339
Metadata
- Authors
- Barron, J. T., & Malik, J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2015-08-01
- Volume
- 37
- Issue
- 8
- Pages
- 1670-1687
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2027560260
- Cited by
- 656
- References
- 60
- FWCI
- 101.18
- Type
- journal article
- Language
- en
- Open access
- closed
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