Fig. 1. From a single flash photograph of a material sample (insets), our deep learning approach predicts a spatially-varying BRDF. See supplemental materials for animations with a moving light.Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional re ectance distribution functions (SVBRDFs) from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle lightweight appearance capture by training a deep neural network to automatically extract and make sense of these visual cues. Once trained, our network is capable of recovering per-pixel normal, di use albedo, specular albedo and specular roughness from a single picture of a at surface lit by a hand-held ash. We achieve this goal by introducing several innovations on training data acquisition and network design. For training, we leverage a large dataset of artist-created, procedural SVBRDFs which we sample and render under multiple lighting directions. We further amplify the data by material mixing to cover a wide diversity of shading e ects, which allows our network to work across many material classes. Motivated by the observation that distant regions of a material sample o en o er complementary visual cues, we design a network that combines an encoder-decoder convolutional track for local feature extraction with a fully-connected track for global feature extraction and propagation. Many important material e ects are view-dependent, and as such ambiguous when observed in a single image. We tackle this challenge by de ning the loss as a di erentiable SVBRDF similarity metric that compares the renderings of the predicted maps against renderings of the ground truth from several lighting and viewing directions. Combined together, these novel ingredients bring clear improvement over state of the art methods for single-shot capture of spatially varying BRDFs.is is the author's version of the work. It is posted here for your personal use. Not for redistribution. e de nitive version was published in ACM Trans.

Empowered by deep learning, recent methods for material capture can estimate a spatially‐varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization‐based approaches. However, a single image is often simply not enough to observe the rich appearance of real‐world materials. We present a deep‐learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order‐independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images ‐ a sweet spot between existing single‐image and complex multi‐image approaches.

SVBRDFs to a target picture. This approach allows the capture of large planar surfaces taken with ambient lighting (far left), by extracting the SVBRDF exemplars from close-up flash pictures (lower left), as well as the creation of plausible SVBRDFs from internet pictures by using existing artist-designed materials as exemplars (right). Please see supplemental materials for high-resolution SVBRDF parameter maps and animated renderings of all our results, which give a much better impression of the material properties.