Snapshot Polarimetric Display Inverse Rendering

• URL: http://arxiv.org/abs/2605.24915v1
• Date: Sun, 24 May 2026 07:40:11 GMT
• Title: Snapshot Polarimetric Display Inverse Rendering
• Abstract summary: We introduce polarimetric display rendering inverse, using an LCD to project a linearly polarized RGB binary pattern and an RGB polarization camera augmented with a quarter-wave plate.<n>A feed-forward transformer maps these measurements to per-pixel normal, albedo, roughness, and metallicity.<n> Evaluations on a real desktop setup demonstrate accurate inverse rendering across diverse scenes, outperforming existing approaches.
• Score: 30.986440347489914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• DensePol: Dense-Angle Polarization Dataset for Learning-Based Polarimetric Vision [8.673131921192995]
  We introduce DensePol, a high-redundancy RGB--polarization dataset based on Division-of-Time (DoT) acquisition.<n>Dense angular sampling substantially improves polarization stability, reducing AoLP deviation from $13.36circ$ to $2.21circ$.<n>Experiments demonstrate improved polarization prediction and downstream surface-normal estimation.
  arXiv  Detail & Related papers  (2026-09-08T18:48:33Z)
• Differentiable Polarized Path Tracing [60.271569691679794]
  Physically based differentiable rendering has proven to be a powerful tool for inverse rendering problems.<n>We propose a robust, polarization-aware differentiable path tracing method.<n>This formulation enables efficient and stable optimization of material and lighting parameters in complex scenes.
  arXiv  Detail & Related papers  (2026-07-14T20:58:08Z)
• A Real-world Display Inverse Rendering Dataset [10.544409905036986]
  Inverse rendering aims to reconstruct geometry and reflectance from captured images.<n>There is currently no public real-world dataset captured using display-camera systems.<n>We introduce the first real-world dataset for display-based inverse rendering.
  arXiv  Detail & Related papers  (2025-08-20T04:15:19Z)
• Glossy Object Reconstruction with Cost-effective Polarized Acquisition [41.96986483856648]
  This work introduces a scalable polarization-aided approach that employs cost-effective acquisition tools.<n>The proposed approach represents polarimetric BRDF, Stokes vectors, and polarization states of object surfaces as neural implicit fields.<n>By leveraging fundamental physical principles for the implicit representation of polarization rendering, our method demonstrates superiority over existing techniques.
  arXiv  Detail & Related papers  (2025-04-09T16:38:51Z)
• I$^2$-SLAM: Inverting Imaging Process for Robust Photorealistic Dense SLAM [10.464532720114052]
  Casual video captures often suffer from motion blur and varying appearances, which degrade the final quality of coherent 3D visual representation. We propose integrating the physical imaging into the SLAM system, which employs linear HDR radiance maps to collect measurements.
  arXiv  Detail & Related papers  (2024-07-16T03:31:33Z)
• Differentiable Display Photometric Stereo [15.842538322034537]
  Photometric stereo leverages variations in illumination conditions to reconstruct surface normals. We present differentiable display photometric stereo (DDPS), addressing the design of display patterns. DDPS learns the display patterns that yield accurate normal reconstruction for a target system in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-06-23T07:05:08Z)
• Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering [84.37776381343662]
  Mip-NeRF proposes a multiscale representation as a conical frustum to encode scale information. We propose mip voxel grids (Mip-VoG), an explicit multiscale representation for real-time anti-aliasing rendering. Our approach is the first to offer multiscale training and real-time anti-aliasing rendering simultaneously.
  arXiv  Detail & Related papers  (2023-04-20T04:05:22Z)
• IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes [99.76677232870192]
  We show how a dense vision transformer, IRISformer, excels at both single-task and multi-task reasoning required for inverse rendering. Specifically, we propose a transformer architecture to simultaneously estimate depths, normals, spatially-varying albedo, roughness and lighting from a single image of an indoor scene. Our evaluations on benchmark datasets demonstrate state-of-the-art results on each of the above tasks, enabling applications like object insertion and material editing in a single unconstrained real image.
  arXiv  Detail & Related papers  (2022-06-16T19:50:55Z)
• Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo [103.08512487830669]
  We present a modern solution to the multi-view photometric stereo problem (MVPS) We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry. Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
  arXiv  Detail & Related papers  (2021-10-11T20:20:03Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials [65.95928593628128]
  We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique. Our algorithm is suitable for perspective cameras and nearby point light sources.
  arXiv  Detail & Related papers  (2020-01-18T12:26:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.