We present a neural latent representation for BRDFs and a BRDF layering network based on it. Our method is able to produce closely matching layered results to the Monte Carlo simulation in Guo et al. [2018] with less cost, and works well with spatially-varying parameters.

Abstract

Bidirectional reflectance distribution functions (BRDFs) are pervasively used in computer graphics to produce realistic physically based appearance. Many common materials in the real world have more than one layer, like wood, skin, car paint, and many decorative materials. However, precise simulation of layered material optics is non-trivial. The most accurate approaches rely on Monte Carlo random walks to simulate the light transport within the layers, leading to high variance and cost. Other approaches are efficient, but less accurate. In this paper, we propose to perform layering in the neural space, by compressing BRDFs into latent codes via a proposed representation neural network, and performing a learned layering operation on these latent vectors via a layering network. Our BRDF evaluation is noise-free and computationally efficient, compared to the state-of-the-art approach; it is also a first step towards a "neural algebra" of operations on BRDFs in a latent space.

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BibTex

@inproceedings{Fan:2022:NLBRDF,
  title={Neural Layered BRDFs},
  author={Jiahui Fan and Beibei Wang and Milo\v{s} Ha\v{s}an and Jian Yang and Ling-Qi Yan},
  booktitle={Proceedings of SIGGRAPH 2022},
  year={2022}
}

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