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Intuitive editing of material appearance

Published: 24 July 2016 Publication History

Abstract

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this work, we develop a novel methodology for intuitive and predictable editing of captured BRDF data, which allows for artistic creation of plausible material appearances, bypassing the difficulty of acquiring novel samples. We synthesize novel materials, and extend the existing MERL dataset [Matusik et al. 2003] up to 400 mathematically valid BRDFs. We design a large-scale experiment with 400 participants, gathering 56000 ratings about the perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals that map the high-level perceptual attributes to an underlying PCA-based representation of BRDFs.
We show how our approach allows for intuitive edits of a wide range of visual properties, and demonstrate through a user study that our functionals are excellent predictors of the perceived attributes of appearance, enabling predictable editing with our framework.

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References

[1]
Matusik, W., Pfister, H., Brand, M., and McMillan, L. 2003. A data-driven reflectance model. ACM Transactions on Graphics 22, 3 (July), 759--769.
[2]
Ngan, A., Durand, F., and Matusik, W. 2005. Experimental analysis of brdf models. In Proceedings of the Sixteenth Eurographics Conference on Rendering Techniques, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, EGSR '05, 117--126.
[3]
Nielsen, J. B., Jensen, H. W., and Ramamoorthi, R. 2015. On optimal, minimal brdf sampling for reflectance acquisition. ACM Transactions on Graphics (TOG) 34, 6 (November).

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  1. Intuitive editing of material appearance

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    cover image ACM Conferences
    SIGGRAPH '16: ACM SIGGRAPH 2016 Posters
    July 2016
    170 pages
    ISBN:9781450343718
    DOI:10.1145/2945078
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 24 July 2016

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    Author Tags

    1. BRDF editing
    2. appearance editing
    3. light reflection models
    4. visual perception

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    Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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