A Signal-Processing Framework for Inverse Rendering

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A Signal-Processing Framework for Inverse Rendering (2001)

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by Ravi Ramamoorthi , Pat Hanrahan

Venue:	In SIGGRAPH 01
Citations:	248 - 21 self

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@INPROCEEDINGS{Ramamoorthi01asignal-processing,
    author = {Ravi Ramamoorthi and Pat Hanrahan},
    title = {A Signal-Processing Framework for Inverse Rendering},
    booktitle = {In SIGGRAPH 01},
    year = {2001},
    pages = {117--128}
}

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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.

Keyphrases

inverse rendering signal-processing framework high-quality data real photograph computer-generated image accurate input model reflected light field new practical representation coherent mathematical framework general illumination condition scene attribute certain problem spherical harmonic coefficient small number main contribution previous empirical result

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