Neural Enhancement of Analytical Appearance Models

2026-04-27

AI Summary

Traditional analytical appearance models suffer from limited expressiveness, while purely neural approaches often exhibit poor generalization and high computational costs. This work proposes a neural-augmented framework that replaces key nodes in the computational graph of standard BRDF models with lightweight multilayer perceptrons, significantly enhancing fitting capacity while preserving the interpretability and structural integrity of the original analytical formulation. The method innovatively combines hypercube search with differentiable optimization to automatically identify optimal augmentation locations. Extensive evaluation demonstrates that the proposed approach outperforms existing methods for reflectance and bidirectional texture function fitting in terms of accuracy, compactness, and efficiency, while remaining fully compatible with mainstream rendering pipelines.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Traditional analytical reflectance models, while compact and interpretable, lack the capacity to accurately represent physical measurements. Recent neural models, which closely fit input data, are less generalizable and often more expensive to store and evaluate. To combine the strengths and overcome the limitations of these two classes of models, we present neural enhancement, a novel framework to boost an input analytical appearance model, by identifying and replacing its key computational nodes/operators with small-scale multi-layer perceptrons. This allows us to leverage the computational graph structure of the original model, while improving its expressiveness at a modest cost. To make the enhancement computationally tractable, we propose a hypercube-based search to automatically and efficiently identify the node(s) and/or operator(s) to be replaced towards maximal gain in a differentiable fashion. We enhance a number of common analytical BRDF models. The results are, at once accurate, compact and efficient, and compare favorably with state-of-the-art work on fitting measured reflectance and bidirectional texture functions. Finally, our models are fully compatible with any standard rasterization or ray-tracing pipeline.

Problem

• analytical reflectance models
• neural models
• BRDF
• appearance modeling
• generalizability

Innovation

• neural enhancement
• analytical BRDF models
• multi-layer perceptron
• differentiable search
• appearance modeling