research-article

genBRDF: discovering new analytic BRDFs with genetic programming

Authors:

Jason Lawrence,

Westley WeimerAuthors Info & Claims

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

Article No.: 114, Pages 1 - 11

https://doi.org/10.1145/2601097.2601193

Published: 27 July 2014 Publication History

Abstract

We present a framework for learning new analytic BRDF models through Genetic Programming that we call genBRDF. This approach to reflectance modeling can be seen as an extension of traditional methods that rely either on a phenomenological or empirical process. Our technique augments the human effort involved in deriving mathematical expressions that accurately characterize complex high-dimensional reflectance functions through a large-scale optimization. We present a number of analysis tools and data visualization techniques that are crucial to sifting through the large result sets produced by genBRDF in order to identify fruitful expressions. Additionally, we highlight several new models found by genBRDF that have not previously appeared in the BRDF literature. These new BRDF models are compact and more accurate than current state-of-the-art alternatives.

Supplementary Material

MP4 File (a114-sidebyside.mp4)

Download
10.61 MB

References

[1]

Aho, A. V., Sethi, R., and Ullman, J. D. 1986. Compilers principles, techniques, and tools. Addison-Wesley, Reading, MA.

Digital Library

[2]

Andalon-Garcia, I. R., and Chavoya-Pena, A. 2012. Performance comparison of three topologies of the island model of a parallel genetic algorithm implementation on a cluster platform. In CONIELECOMP, 1--6.

[3]

Ashikhmin, M., Premoze, S., and Shirley, P. 2000. A microfacet-based BRDF generator. In Siggraph 2000, Computer Graphics Proceedings, 65--74.

Digital Library

[4]

Bagher, Mahdi, M., Soler, C., and Holzschuch, N. 2012. Accurate fitting of measured reflectances using a Shifted Gamma micro-facet distribution. Computer Graphics Forum 31, 4 (June), 1509--1518.

Digital Library

[5]

Beckmann, P., and Spizzichino, A. 1963. The scattering of electromagnetic waves from rough surfaces. International series of monographs on electromagnetic waves. Pergamon Press.

[6]

Blinn, J. F. 1977. Models of light reflection for computer synthesized pictures. SIGGRAPH Comput. Graph. 11, 2 (July), 192--198.

Digital Library

[7]

Cook, R. L., and Torrance, K. E. 1982. A reflectance model for computer graphics. ACM Trans. Graph. 1, 1, 7--24.

Digital Library

[8]

Dorsey, J., Rushmeier, H., and Sillion, F. 2008. Digital Modeling of Material Appearance. Morgan Kaufmann Publishers Inc.

Digital Library

[9]

Grosso, P. B. 1985. Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model. PhD thesis, Ann Arbor, MI, USA.

Digital Library

[10]

He, X. D., Torrance, K. E., Sillion, F., and Greenberg, D. P. 1991. A comprehensive physical model for light reflection. In SIGGRAPH'91 conference proceedings.

Digital Library

[11]

Holland, J. H. 1992. Adaptation in natural and artificial systems. MIT Press, Cambridge, MA, USA.

Digital Library

[12]

Jakob, W., 2010. Mitsuba renderer. http://www.mitsuba-renderer.org.

[13]

Kelemen, C., Szirmay-Kalos, L., and Szirmay-kalos, L. 2001. A microfacet based coupled specular-matte brdf model with importance sampling. In Eurographics (short presentations).

Digital Library

[14]

Koza, J. R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.

Digital Library

[15]

Löw, J., Kronander, J., Ynnerman, A., and Unger, J. 2012. Brdf models for accurate and efficient rendering of glossy surfaces. ACM Transactions on Graphics (TOG) 31, 1 (January).

Digital Library

[16]

Matusik, W., Pfister, H., Brand, M., and McMillan, L. 2003. A data-driven reflectance model. ACM Trans. Graph. 22, 3, 759--769.

Digital Library

[17]

Miller, B. L., Miller, B. L., Goldberg, D. E., and Goldberg, D. E. 1995. Genetic algorithms, tournament selection, and the effects of noise. Complex Systems 9, 193--212.

[18]

Nelder, J. A., and Mead, R. 1965. A simplex method for function minimization. The computer journal 7, 4, 308--313.

[19]

Ngan, A., Durand, F., and Matusik, W. 2005. Experimental analysis of BRDF models. Eurographics Symposium on Rendering 2005, 117--226.

Digital Library

[20]

Nicodemus, F. E., Richmond, J. C., Hsia, J. J., Ginsberg, I. W., and Limperis, T. 1977. Geometric considerations and nomenclature for reflectance. Monograph 161, National Bureau of Standards (US).

[21]

Oren, M., and Nayar, S. K. 1994. Generalization of lambert's reflectance model. In Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, 239--246.

Digital Library

[22]

Pereira, T., and Rusinkiewicz, S. 2012. Gamut mapping spatially varying reflectance with an improved brdf similarity metric. Comput. Graph. Forum 31, 4, 1557--1566.

Digital Library

[23]

Phong, B. T. 1975. Illumination for computer generated pictures. Commun. ACM 18, 6, 311--317.

Digital Library

[24]

Schlick, C. 1994. An inexpensive brdf model for physically-based rendering. Computer Graphics Forum 13, 3, 233--246.

[25]

Sharma, G., Wu, W., and Dalal, E. N. 2005. The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research & Application 30, 1, 21--30.

[26]

Sitthi-Amorn, P., Modly, N., Weimer, W., and Lawrence, J. 2011. Genetic programming for shader simplification. ACM Trans. Graph. 30, 6 (Dec.).

Digital Library

[27]

Torrance, K. E., and Sparrow, E. M. 1992. Radiometry. ch. Theory for off-specular reflection from roughened surfaces, 32--41.

Digital Library

[28]

Walter, B., Marschner, S. R., Li, H., and Torrance, K. E. 2007. Microfacet models for refraction through rough surfaces. In Rendering Techniques, 195--206.

Digital Library

[29]

Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Proc. 13, 4, 600--612.

Digital Library

[30]

Ward, G. 1992. Measuring and modeling anisotropic reflection. In ACM SIGGRAPH Computer Graphics, vol. 26, 265--272.

Digital Library

Cited By

Tamisier ERibardière MMeneveaux DHorna SPoulin P(2024)A data-driven correction for the masking model of SmithJournal of the Optical Society of America A10.1364/JOSAA.530840Online publication date: 2-Aug-2024
https://doi.org/10.1364/JOSAA.530840
Atanasov AKoylazov VDimov RWilkie A(2022)Microsurface TransformationsComputer Graphics Forum10.1111/cgf.1459041:4(105-116)Online publication date: 30-Jul-2022
https://doi.org/10.1111/cgf.14590
Shi WDorsey JRushmeier H(2022)Learning-Based Inverse Bi-Scale Material Fitting From Tabular BRDFsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.302602128:4(1810-1823)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TVCG.2020.3026021
Show More Cited By

Index Terms

genBRDF: discovering new analytic BRDFs with genetic programming
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Texturing

Recommendations

Generalization of Lambert's reflectance model
SIGGRAPH '94: Proceedings of the 21st annual conference on Computer graphics and interactive techniques

Lambert's model for body reflection is widely used in computer graphics. It is used extensively by rendering techniques such as radiosity and ray tracing. For several real-world objects, however, Lambert's model can prove to be a very inaccurate ...
Printing reflectance functions

The reflectance function of a scene point captures the appearance of that point as a function of lighting direction. We present an approach to printing the reflectance functions of an object or scene so that its appearance is modified correctly as a ...
All-frequency relighting of glossy objects

We present a technique for interactive rendering of glossy objects in complex and dynamic lighting environments that captures interreflections and all-frequency shadows. Our system is based on precomputed radiance transfer and separable BRDF ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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

Issue’s Table of Contents

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2014

Published in TOG Volume 33, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

41
Total Citations
View Citations
769
Total Downloads

Downloads (Last 12 months)23
Downloads (Last 6 weeks)2

Reflects downloads up to 25 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tamisier ERibardière MMeneveaux DHorna SPoulin P(2024)A data-driven correction for the masking model of SmithJournal of the Optical Society of America A10.1364/JOSAA.530840Online publication date: 2-Aug-2024
https://doi.org/10.1364/JOSAA.530840
Atanasov AKoylazov VDimov RWilkie A(2022)Microsurface TransformationsComputer Graphics Forum10.1111/cgf.1459041:4(105-116)Online publication date: 30-Jul-2022
https://doi.org/10.1111/cgf.14590
Shi WDorsey JRushmeier H(2022)Learning-Based Inverse Bi-Scale Material Fitting From Tabular BRDFsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.302602128:4(1810-1823)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TVCG.2020.3026021
Zheng CZheng RWang RZhao SBao H(2021)A Compact Representation of Measured BRDFs Using Neural ProcessesACM Transactions on Graphics10.1145/349038541:2(1-15)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3490385
Lavoué GBonneel NFarrugia JSoler C(2021)Perceptual quality of BRDF approximations: dataset and metricsComputer Graphics Forum10.1111/cgf.14263640:2(327-338)Online publication date: 4-Jun-2021
https://doi.org/10.1111/cgf.142636
Bernardi AGadia DMaggiorini DPalazzi CRipamonti L(2021)Procedural generation of materials for real-time renderingMultimedia Tools and Applications10.1007/s11042-020-09141-980:9(12969-12990)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s11042-020-09141-9
Sole AGuarnera GFarup INussbaum P(2021)Measurement and rendering of complex non-diffuse and goniochromatic packaging materialsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-01980-937:8(2207-2220)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00371-020-01980-9
Kurt M(2021)GenSSS: a genetic algorithm for measured subsurface scattering representationThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-01800-037:2(307-323)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s00371-020-01800-0
Bieron JPeers P(2020)An Adaptive BRDF Fitting MetricComputer Graphics Forum10.1111/cgf.1405439:4(59-74)Online publication date: 20-Jul-2020
https://doi.org/10.1111/cgf.14054
Rainer GGhosh AJakob WWeyrich T(2020)Unified Neural Encoding of BTFsComputer Graphics Forum10.1111/cgf.1392139:2(167-178)Online publication date: 13-Jul-2020
https://doi.org/10.1111/cgf.13921
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents