Neural Enhancement of Analytical Appearance Models.

Area of Science:

• Computer Graphics
• Material Appearance Modeling

Background:

• Traditional analytical reflectance models are interpretable but inaccurate.
• Neural network models are accurate but less generalizable and computationally expensive.

Purpose of the Study:

• To combine the strengths of analytical and neural models for reflectance.
• To develop a novel framework called neural enhancement for appearance models.

Main Methods:

• Replacing key computational nodes/operators in analytical models with small multi-layer perceptrons.
• Utilizing a hypercube-based search for efficient and differentiable node/operator identification.
• Enhancing common analytical Bidirectional Reflectance Distribution Function (BRDF) models.

Main Results:

• Achieved accurate, compact, and efficient enhanced models.
• Demonstrated favorable comparison with state-of-the-art reflectance fitting methods.
• Ensured compatibility with standard rasterization and ray-tracing pipelines.

Conclusions:

• Neural enhancement effectively boosts analytical appearance models.
• The proposed method offers a cost-effective way to improve model expressiveness.
• Enhanced models provide accurate and efficient reflectance representation for computer graphics.