Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image

Zhengqin Li, Kalyan Sunkavalli, Manmohan Chandraker; The European Conference on Computer Vision (ECCV), 2018, pp. 72-87


We propose a material acquisition system that can 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 technique 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 classification task to provide additional supervision during training. Finally, we refine the results from the network using a dense CRF module whose terms are designed specifically for our task. We demonstrate that our CNN-based SVBRDF inference leads to state-of-the-art results on a wide variety of materials on both synthetic and real data. We also provide extensive ablation studies to evaluate our network and demonstrate large improvements in comparisons with prior works.

Related Material

author = {Li, Zhengqin and Sunkavalli, Kalyan and Chandraker, Manmohan},
title = {Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}