Neural Reflectance Capture in the View-Illumination Domain.

We propose a novel framework to efficiently capture the unknown reflectance on a non-planar 3D object, by learning to probe the 4D view-lighting domain with a high-performance illumination multiplexing setup. The core of our framework is a deep neural network, specifically tailored to exploit the multi-view coherence for efficiency. It takes as input the photometric measurements of a surface point under learned lighting patterns at different views, automatically aggregates the information and reconstructs the anisotropic reflectance. We also evaluate the impact of different sampling parameters over our network. The effectiveness of our framework is demonstrated on high-quality reconstructions of a variety of physical objects, with an acquisition efficiency outperforming state-of-the-art techniques.