A gaussian process latent variable model for BRDF inference

© 2015 IEEE. The problem of estimating a full BRDF from partial observations has already been studied using either parametric or non-parametric approaches. The goal in each case is to best match this sparse set of input measurements. In this paper we address the problem of inferring higher order reflectance information starting from the minimal input of a single BRDF slice. We begin from the prototypical case of a homogeneous sphere, lit by a head-on light source, which only holds information about less than 0.001% of the whole BRDF domain. We propose a novel method to infer the higher dimensional properties of the material's BRDF, based on the statistical distribution of known material characteristics observed in real-life samples. We evaluated our method based on a large set of experiments generated from real-world BRDFs and newly measured materials. Although inferring higher dimensional BRDFs from such modest training is not a trivial problem, our method performs better than state-of-the-art parametric, semi-parametric and non-parametric approaches. Finally, we discuss interesting applications on material re-lighting, and flash-based photography.Georgoulis S., Vanweddingen V., Proesmans M., Van Gool L., ''A gaussian process latent variable model for BRDF inference'', Proceedings 15th international conference on computer vision - ICCV 2015, pp. 3559-3567, December 11-18, 2015, Santiago, Chile.status: publishe