The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A signal-processing framework for inverse rendering
Abstract
Realism in computer-generated images requires accurate input models for lighting, textures and BRDFs. One of the best ways of obtaining high-quality data is through measurements of scene attributes from real photographs by inverse rendering. However, inverse rendering methods have been largely limited to settings with highly controlled lighting. One of the reasons for this is the lack of a coherent mathematical framework for inverse rendering under general illumination conditions. Our main contribution is the introduction of a signal-processing framework which describes the reflected light field as a convolution of the lighting and BRDF, and expresses it mathematically as a product of spherical harmonic coefficients of the BRDF and the lighting. Inverse rendering can then be viewed as deconvolution. We apply this theory to a variety of problems in inverse rendering, explaining a number of previous empirical results. We will show why certain problems are ill-posed or numerically ill-conditioned, and why other problems are more amenable to solution. The theory developed here also leads to new practical representations and algorithms. For instance, we present a method to factor the lighting and BRDF from a small number of views, i.e. to estimate both simultaneously when neither is known.
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Cites
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The digital Michelangelo project
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Face2Face: Real-Time Face Capture and Reenactment of RGB Videos
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., & Niessner, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.262 · Cited by 1,747
Gradient domain high dynamic range compression
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Clustering appearances of objects under varying illumination conditions
Ho, J., Yang, M.-H., Lim, J., Lee, K.-C., & Kriegman, D. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211332 · Cited by 521
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
Total variation models for variable lighting face recognition
Chen, T., Yin, W., Zhou, X. S., Comaniciu, D., & Huang, T. S. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.195 · Cited by 437
Metadata
- Authors
- Ramamoorthi, R., & Hanrahan, P.
- Venue
- Proceedings of the 28th annual conference on Computer graphics and interactive techniques
- Publisher
- ACM
- Published
- 2001-08
- Pages
- 117-128
- OpenAlex
- W2099478163
- Cited by
- 577
- References
- 22
- FWCI
- 16.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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