MIPNet: Neural Normal-to-Anisotropic-Roughness MIP mapping - Equipe Image, Modélisation, Analyse, GEométrie, Synthèse Accéder directement au contenu
Article Dans Une Revue ACM Transactions on Graphics Année : 2022

MIPNet: Neural Normal-to-Anisotropic-Roughness MIP mapping

Résumé

We present MIPNet, a novel approach for SVBRDF mipmapping which preserves material appearance under varying view distances and lighting conditions. As in classical mipmapping, our method explicitly encodes the multiscale appearance of materials in a SVBRDF mipmap pyramid. To do so, we use a tensor-based representation, coping with gradient-based optimization, for encoding anisotropy which is compatible with existing real-time rendering engines. Instead of relying on a simple texture patch average for each channel independently, we propose a cascaded architecture of multilayer perceptrons to approximate the material appearance using only the fixed material channels. Our neural model learns simple mipmapping filters using a differentiable rendering pipeline based on a rendering loss and is able to transfer signal from normal to anisotropic roughness. As a result, we obtain a drop-in replacement for standard material mipmapping, offering a significant improvement in appearance preservation while still boiling down to a single per-pixel mipmap texture fetch. We report extensive experiments on two distinct BRDF models.
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Dates et versions

hal-04001287 , version 1 (22-02-2023)

Identifiants

Citer

Alban Gauthier, Robin Faury, Jérémy Levallois, Théo Thonat, Jean-Marc Thiery, et al.. MIPNet: Neural Normal-to-Anisotropic-Roughness MIP mapping. ACM Transactions on Graphics, 2022, 41 (6), pp.1-12. ⟨10.1145/3550454.3555487⟩. ⟨hal-04001287⟩
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