Neural Appearance Modeling From Single Images
This addresses material modeling for computer graphics applications, offering a novel neural approach but is incremental in leveraging existing neural architectures for a specific bottleneck.
The authors tackled the problem of estimating spatially-varying material appearance from a single photograph under co-located light and view, achieving plausible visualization under diverse conditions by training on 312,165 synthetic exemplars and integrating into the Mitsuba3 rendering engine.
We propose a material appearance modeling neural network for visualizing plausible, spatially-varying materials under diverse view and lighting conditions, utilizing only a single photograph of a material under co-located light and view as input for appearance estimation. Our neural architecture is composed of two network stages: a network that infers learned per-pixel neural parameters of a material from a single input photograph, and a network that renders the material utilizing these neural parameters, similar to a BRDF. We train our model on a set of 312,165 synthetic spatially-varying exemplars. Since our method infers learned neural parameters rather than analytical BRDF parameters, our method is capable of encoding anisotropic and global illumination (inter-pixel interaction) information into individual pixel parameters. We demonstrate our model's performance compared to prior work and demonstrate the feasibility of the render network as a BRDF by implementing it into the Mitsuba3 rendering engine. Finally, we briefly discuss the capability of neural parameters to encode global illumination information.