Hyper-SNBRDF: Hypernetwork for Neural BRDF Using Sinusoidal Activation

Densely captured real-world materials require effective compression for rendering, material generation and reconstruction. Neural networks with high compression rates and the ability to fit complex functions can encode each BRDF into the corresponding network. However, current works that take advantage of single implicit neural representations are incapable of effectively modeling the high-frequency details of the highlight region. In this paper, we propose an improved compact neural network representation of BRDF data based on the sinusoidal activation. The lightweight network and the periodic activation function improve the fidelity of the reproduction material appearance under the condition of a high compression rate. Furthermore, the method of building a unified model using neural networks can decode all materials from latent space. However, the deep structure of the network model increases memory consumption. To overcome this challenge, we propose a hypernetwork framework that compresses measured BRDFs to latent space and generates weights for the neural network-based representation of materials. The lightweight implicit representation of BRDF generated by training directly from original materials shows the characteristics of a low memory footprint and high-precision reproduction of appearance. Additionally, we apply the hypernetwork to reconstruct materials from a single image. Thanks to implicit representation of BRDF that can reproduce the appearance with high fidelity, the reflectance properties can be accurately recovered.