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.

翻译：传统分析反射模型虽紧凑且可解释，但缺乏准确表征物理测量的能力。近期神经模型虽能紧密拟合输入数据，但泛化性较弱，且存储与评估成本通常更高。为融合两类模型优势并克服其局限，我们提出神经增强——一种通过识别并替换输入分析外观模型中关键计算节点/算子为小型多层感知器的新型框架。该方法在保留原始模型计算图结构的同时，以适度成本提升其表达能力。为实现可计算性增强，我们提出基于超立方体的自动搜索方法，以可微分方式高效识别需替换的节点和/或算子以获取最大增益。我们对多个常见分析BRDF模型进行增强，所得模型兼具精确性、紧凑性与高效性，在拟合实测反射率与双向纹理函数方面优于现有最优方法。最终，我们的模型与标准光栅化或光线追踪管线完全兼容。