Abstract. 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 highfrequency 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