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Found 5 papers, 2 papers with code
TexTile: A Differentiable Metric for Texture Tileability
We introduce TexTile, a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i. e., the tileability).
NEnv: Neural Environment Maps for Global Illumination
We propose NEnv, a deep-learning fully-differentiable method, capable of compressing and learning to sample from a single environment map.
NeuBTF: Neural fields for BTF encoding and transfer
However, existing neural materials are immutable, meaning that their output for a certain query of UVs, camera, and light vector is fixed once they are trained.
A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond
Intrinsic imaging or intrinsic image decomposition has traditionally been described as the problem of decomposing an image into two layers: a reflectance, the albedo invariant color of the material; and a shading, produced by the interaction between light and geometry.
BRDF Estimation of Complex Materials with Nested Learning
The estimation of the optical properties of a material from RGB-images is an important but extremely ill-posed problem in Computer Graphics.
