Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis

Snapshot polarization imaging calculates polarization states from linearly polarized subimages. To achieve this, a polarization camera employs a double Bayer-patterned sensor to capture both color and polarization. It demonstrates low light efficiency and low spatial resolution, resulting in increased noise and compromised polarization measurements. Although burst super-resolution effectively reduces noise and enhances spatial resolution, applying it to polarization imaging poses challenges due to the lack of tailored datasets and reliable ground truth noise statistics. To address these issues, we introduce PolarNS and PolarBurstSR, two innovative datasets developed specifically for polarization imaging. PolarNS provides characterization of polarization noise statistics, facilitating thorough analysis, while PolarBurstSR functions as a benchmark for burst super-resolution in polarization images. These datasets, collected under various real-world conditions, enable comprehensive evaluation. Additionally, we present a model for analyzing polarization noise to quantify noise propagation, tested on a large dataset captured in a darkroom environment. As part of our application, we compare the latest burst super-resolution models, highlighting the advantages of training tailored to polarization compared to RGB-based methods. This work establishes a benchmark for polarization burst super-resolution and offers critical insights into noise propagation, thereby enhancing polarization image reconstruction.

Polarimetric BSSRDF Acquisition of Dynamic Faces

Acquisition and modeling of polarized light reflection and scattering help reveal the shape, structure, and physical characteristics of an object, which is increasingly important in computer graphics. However, current polarimetric acquisition systems are limited to static and opaque objects. Human faces, on the other hand, present a particularly difficult challenge, given their complex structure and reflectance properties, the strong presence of spatially-varying subsurface scattering, and their dynamic nature. We present a new polarimetric acquisition method for dynamic human faces, which focuses on capturing spatially varying appearance and precise geometry, across a wide spectrum of skin tones and facial expressions. It includes both single and heterogeneous subsurface scattering, index of refraction, and specular roughness and intensity, among other parameters, while revealing biophysically-based components such as inner- and outer-layer hemoglobin, eumelanin and pheomelanin. Our method leverages such components' unique multispectral absorption profiles to quantify their concentrations, which in turn inform our model about the complex interactions occurring within the skin layers. To our knowledge, our work is the first to simultaneously acquire polarimetric and spectral reflectance information alongside biophysically-based skin parameters and geometry of dynamic human faces. Moreover, our polarimetric skin model integrates seamlessly into various rendering pipelines.

Sparse Ellipsometry: Portable Acquisition of Polarimetric SVBRDF and Shape with Unstructured Flash Photography

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.