Sparse SVBRDF Acquisition via Importance-Aware Illumination Multiplexing

Reflectance acquisition from sparse images has been a long-standing problem in computer graphics. Previous works have addressed this by introducing either material-related priors or illumination multiplexing with a general sampling strategy. However, fixed lighting patterns in multiplexing can lead to redundant sampling and entangled observations, making it necessary to adaptively capture salient reflectance responses in each shot based on material behavior. In this paper, we propose combining adaptive sampling with illumination multiplexing for SVBRDF reconstruction from sparse images lit by a planar light source. Central to our method is the modeling of a sampling importance distribution on lighting surface, guided by the statistical nature of microfacet theory. Based on this sampling structure, our framework jointly trains networks to learn an adaptive sampling strategy in the lighting domain, and furthermore, approximately separates pure specular-related information from observations to reduce ambiguities in reconstruction. We validate our approach through experiments and comparisons with previous works on both synthetic and real materials.