Snapshot Polarimetric Display Inverse Rendering
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Abstract (arXiv)
Inverse rendering remains a core challenge in graphics and vision, especially in the snapshot configurations required for lightweight desktop workflows, where the per-frame information budget is highly constrained. Previous inverse rendering work explores various available dimensions for enriching the per-shot information, including temporal modulation, spectral encoding, and polarization. In this work, we introduce polarimetric display inverse rendering, using an LCD to project a linearly polarized RGB binary pattern and an RGB polarization camera augmented with a quarter-wave plate to acquire spectro-polarimetric measurements in a single shot. A feed-forward transformer maps these measurements to per-pixel normal, albedo, roughness, and metallicity. To overcome training data scarcity, we expand a limited set of measured polarimetric bidirectional reflectance distribution functions via a generative manifold. Evaluations on a real desktop setup demonstrate accurate inverse rendering across diverse scenes, outperforming existing approaches.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 61 references that are in the map. First posted on arXiv 24 May 2026.
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29 of 61 references.
References · 29
- 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2021Vision Transformers for Dense Predictioncited by 1,803
- 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,540
- 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 335
- 2022Modeling Indirect Illumination for Inverse Renderingcited by 168
- 2023TensoIR: Tensorial Inverse Renderingcited by 168
- 2024RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion modelscited by 161
- 2022IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Imagescited by 111
- 2022PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereocited by 69
- 2024IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illuminationcited by 67
- 2021Deep Polarization Imaging for 3D shape and SVBRDF Acquisitioncited by 56
17 more
- 2021Shape and Material Capture at Homecited by 36
- 2023Scalable, Detailed and Mask-Free Universal Photometric Stereocited by 30
- 2022Neural Reflectance for Shape Recovery with Shadow Handlingcited by 28
- 2023NeISF: Neural Incident Stokes Field for Geometry and Material Estimationcited by 25
- 2024Neural LightRig: Unlocking Accurate Object Normal and Material Estimation with Multi-Light Diffusioncited by 19
- 2025LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fieldscited by 16
- 2024PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objectscited by 15
- 2024Uni-Renderer: Unifying Rendering and Inverse Rendering Via Dual Stream Diffusioncited by 15
- 2024NeISF++: Neural Incident Stokes Field for Polarized Inverse Rendering of Conductors and Dielectricscited by 9
- 2025Light of Normals: Unified Feature Representation for Universal Photometric Stereocited by 9
- 2023Differentiable Point-based Inverse Renderingcited by 8
- 2023SPIDeRS: Structured Polarization for Invisible Depth and Reflectance Sensingcited by 6
- 2024Differentiable Inverse Rendering with Interpretable Basis BRDFscited by 6
- 2023Differentiable Display Photometric Stereocited by 4
- 2024Physics-Free Spectrally Multiplexed Photometric Stereo under Unknown Spectral Compositioncited by 4
- 2025A Real-world Display Inverse Rendering Datasetcited by 4
- 2025Hyperspectral Polarimetric BRDFs of Real-world Materialscited by 1
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