Sparse Ellipsometry: Portable Acquisition of Polarimetric SVBRDF and Shape with Unstructured Flash Photography
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 7 of its 68 references that are in the map. First posted on arXiv 9 Jul 2022.
Builds on
7 of 68 references.
References · 7
- 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 496
- 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 449
- 2021Deep Polarization Imaging for 3D shape and SVBRDF Acquisitioncited by 56
- 2021Shape from Polarization for Complex Scenes in the Wildcited by 53
- 2019Depth from a polarisation + RGB stereo paircited by 29
- 2020Polarimetric Multi-View Inverse Renderingcited by 28
- 2017Linear Differential Constraints for Photo-polarimetric Height Estimationcited by 9
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 1.
7 papers.
- 2023UMat: Uncertainty-Aware Single Image High Resolution Material Capturecited by 11
- 2023Neural Spectro-polarimetric Fieldscited by 10
- 2022Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflectioncited by 6
- 2024Uncertainty for SVBRDF Acquisition using Frequency Analysiscited by 4
- 2024NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticitycited by 2
- 2024Spin-Weighted Spherical Harmonics for Polarized Light Transportcited by 1
- 2025Spatial Polarization Multiplexing: Single-Shot Invisible Shape and Reflectance Recoverycited by 0
- 2025Spatial Polarization Multiplexing: Single-Shot Invisible Shape and Reflectance Recovery17 Apr 2025
- 2024Spin-Weighted Spherical Harmonics for Polarized Light Transport29 Dec 2024
- 2024Uncertainty for SVBRDF Acquisition using Frequency Analysis25 Jun 2024
- 2024NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity14 Jun 2024
- 2023Neural Spectro-polarimetric Fields21 Jun 2023
- 2023UMat: Uncertainty-Aware Single Image High Resolution Material Capture25 May 2023
- 2022Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection8 Dec 2022