Real-Time Neural BRDF with Spherically Distributed Primitives
Yishun Dou Zhong Zheng Qiaoqiao Jin Bingbing Ni Yugang Chen Junxiang Ke
We propose a novel compact and efficient neural BRDF offering highly
versatile material representation, yet with very-light memory and neural
computation consumption towards achieving real-time rendering. The results in
Figure 1, rendered at full HD resolution on a current desktop machine, show
that our system achieves real-time rendering with a wide variety of
appearances, which is approached by the following two designs. On the one hand,
noting that bidirectional reflectance is distributed in a very sparse
high-dimensional subspace, we propose to project the BRDF into two
low-dimensional components, i.e., two hemisphere feature-grids for incoming and
outgoing directions, respectively. On the other hand, learnable neural
reflectance primitives are distributed on our highly-tailored spherical surface
grid, which offer informative features for each component and alleviate the
conventional heavy feature learning network to a much smaller one, leading to
very fast evaluation. These primitives are centrally stored in a codebook and
can be shared across multiple grids and even across materials, based on the
low-cost indices stored in material-specific spherical surface grids. Our
neural BRDF, which is agnostic to the material, provides a unified framework
that can represent a variety of materials in consistent manner. Comprehensive
experimental results on measured BRDF compression, Monte Carlo simulated BRDF
acceleration, and extension to spatially varying effect demonstrate the
superior quality and generalizability achieved by the proposed scheme.