Ellipsometry techniques allow to measure polarization information of materials, requiring precise rotations of optical components with different configurations of lights and sensors. This results in cumbersome capture devices, carefully calibrated in lab conditions, and in very long acquisition times, usually in the order of a few days per object. Recent techniques allow to capture polarimetric spatially-varying reflectance information, but limited to a single view, or to cover all view directions, but limited to spherical objects made of a single homogeneous material. We present sparse ellipsometry , a portable polarimetric acquisition method that captures both polarimetric SVBRDF and 3D shape simultaneously. Our handheld device consists of off-the-shelf, fixed optical components. Instead of days, the total acquisition time varies between twenty and thirty minutes per object. We develop a complete polarimetric SVBRDF model that includes diffuse and specular components, as well as single scattering, and devise a novel polarimetric inverse rendering algorithm with data augmentation of specular reflection samples via generative modeling. Our results show a strong agreement with a recent ground-truth dataset of captured polarimetric BRDFs of real-world objects.

In this paper, we propose two real‐time models for simulating subsurface scattering for a large variety of translucent materials, which need under 0.5 ms per frame to execute. This makes them a practical option for real‐time production scenarios. Current state‐of‐the‐art, real‐time approaches simulate subsurface light transport by approximating the radially symmetric non‐separable diffusion kernel with a sum of separable Gaussians, which requires multiple (up to 12) 1D convolutions. In this work we relax the requirement of radial symmetry to approximate a 2D diffuse reflectance profile by a single separable kernel. We first show that low‐rank approximations based on matrix factorization outperform previous approaches, but they still need several passes to get good results. To solve this, we present two different separable models: the first one yields a high‐quality diffusion simulation, while the second one offers an attractive trade‐off between physical accuracy and artistic control. Both allow rendering of subsurface scattering using only two 1D convolutions, reducing both execution time and memory consumption, while delivering results comparable to techniques with higher cost. Using our importance‐sampling and jittering strategies, only seven samples per pixel are required. Our methods can be implemented as simple post‐processing steps without intrusive changes to existing rendering pipelines.

Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accurately discern the visual properties of an object is a long-standing problem. In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks. We carry out large-scale behavioral experiments where participants are asked to recognize different reference materials among a pool of candidate samples. In the different experiments, we carefully sample the information in the frequency domain of the stimuli. From our analysis, we find significant first-order interactions between the geometry and the illumination, of both the reference and the candidates. In addition, we observe that simple image statistics and higher-order image histograms do not correlate with human performance. Therefore, we perform a high-level comparison of highly nonlinear statistics by training a deep neural network on material recognition tasks. Our results show that such models can accurately classify materials, which suggests that they are capable of defining a meaningful representation of material appearance from labeled proximal image data. Last, we find preliminary evidence that these highly nonlinear models and humans may use similar high-level factors for material recognition tasks.