Abstract
Non-diffuse materials (e.g., metallic inks, varnishes, and paints) are widely used in real-world applications. Accurate spectral rendering relies on the bidirectional reflectance distribution function (BRDF). Current methods of capturing the BRDFs have proven to be onerous in accomplishing quick turnaround time, from conception and design to production. We propose a multi-layer perceptron for compact spectral material representations, with 31 wavelengths for four real-world packaging materials. Our neural-based scenario reduces measurement requirements while maintaining significant saliency. Unlike tristimulus BRDF acquisition, this spectral approach has not, to our knowledge, been previously explored with neural networks. We demonstrate compelling results for diffuse, glossy, and goniochromatic materials.