  We propose a material acquisition approach to recover the spatially-varying
BRDF and normal map of a near-planar surface from a single image captured by a
handheld mobile phone camera. Our method images the surface under arbitrary
environment lighting with the flash turned on, thereby avoiding shadows while
simultaneously capturing high-frequency specular highlights. We train a CNN to
regress an SVBRDF and surface normals from this image. Our network is trained
using a large-scale SVBRDF dataset and designed to incorporate physical
insights for material estimation, including an in-network rendering layer to
model appearance and a material classifier to provide additional supervision
during training. We refine the results from the network using a dense CRF
module whose terms are designed specifically for our task. The framework is
trained end-to-end and produces high quality results for a variety of
materials. We provide extensive ablation studies to evaluate our network on
both synthetic and real data, while demonstrating significant improvements in
comparisons with prior works.
