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arXiv · 2307.10135

A Hierarchical Architecture for Neural Materials

Bowen Xue·Shuang Zhao·Henrik Wann Jensen·Zahra Montazeri
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Abstract

Neural reflectance models are capable of reproducing the spatially-varying appearance of many real-world materials at different scales. Unfortunately, existing techniques such as NeuMIP have difficulties handling materials with strong shadowing effects or detailed specular highlights. In this paper, we introduce a neural appearance model that offers a new level of accuracy. Central to our model is an inception-based core network structure that captures material appearances at multiple scales using parallel-operating kernels and ensures multi-stage features through specialized convolution layers. Furthermore, we encode the inputs into frequency space, introduce a gradient-based loss, and employ it adaptive to the progress of the learning phase. We demonstrate the effectiveness of our method using a variety of synthetic and real examples.

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Bowen Xue, Shuang Zhao, Henrik Wann Jensen, Zahra Montazeri. 2024-04-24. A Hierarchical Architecture for Neural Materials. https://arxiv.org/abs/2307.10135

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