Physics-informed Active Polarimetric 3D Imaging for Specular Surfaces

• URL: http://arxiv.org/abs/2602.19470v1
• Date: Mon, 23 Feb 2026 03:28:41 GMT
• Title: Physics-informed Active Polarimetric 3D Imaging for Specular Surfaces
• Authors: Jiazhang Wang, Hyelim Yang, Tianyi Wang, Florian Willomitzer,
• Abstract summary: We propose a physics-informed deep learning framework for single-shot 3D imaging of complex specular surfaces.<n>The proposed method achieves accurate and robust normal estimation in single-shot with fast inference, enabling practical 3D imaging of complex specular surfaces.
• Score: 4.019683930752727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D imaging of specular surfaces remains challenging in real-world scenarios, such as in-line inspection or hand-held scanning, requiring fast and accurate measurement of complex geometries. Optical metrology techniques such as deflectometry achieve high accuracy but typically rely on multi-shot acquisition, making them unsuitable for dynamic environments. Fourier-based single-shot approaches alleviate this constraint, yet their performance deteriorates when measuring surfaces with high spatial frequency structure or large curvature. Alternatively, polarimetric 3D imaging in computer vision operates in a single-shot fashion and exhibits robustness to geometric complexity. However, its accuracy is fundamentally limited by the orthographic imaging assumption. In this paper, we propose a physics-informed deep learning framework for single-shot 3D imaging of complex specular surfaces. Polarization cues provide orientation priors that assist in interpreting geometric information encoded by structured illumination. These complementary cues are processed through a dual-encoder architecture with mutual feature modulation, allowing the network to resolve their nonlinear coupling and directly infer surface normals. The proposed method achieves accurate and robust normal estimation in single-shot with fast inference, enabling practical 3D imaging of complex specular surfaces.

Related papers

• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• Surf3R: Rapid Surface Reconstruction from Sparse RGB Views in Seconds [34.38496869014632]
  Surf3R is an end-to-end feedforward approach that reconstructs 3D surfaces from sparse views without estimating camera poses.<n>Our method employs a multi-branch and multi-view decoding architecture in which multiple reference views jointly guide the reconstruction process.
  arXiv  Detail & Related papers  (2025-08-06T14:53:42Z)
• PRaDA: Projective Radial Distortion Averaging [40.77624901787694]
  We tackle the problem of automatic calibration of radially distorted cameras in challenging conditions.<n>Our proposed method, Projective Radial Distortion Averaging, averages multiple distortion estimates in a fully projective framework.
  arXiv  Detail & Related papers  (2025-04-23T08:22:59Z)
• AlignDiff: Learning Physically-Grounded Camera Alignment via Diffusion [0.5277756703318045]
  We introduce a novel framework that addresses camera intrinsic and extrinsic parameters using a generic ray camera model.<n>Unlike previous approaches, AlignDiff shifts focus from semantic to geometric features, enabling more accurate modeling of local distortions.<n>Our experiments demonstrate that the proposed method significantly reduces the angular error of estimated ray bundles by 8.2 degrees and overall calibration accuracy, outperforming existing approaches on challenging, real-world datasets.
  arXiv  Detail & Related papers  (2025-03-27T14:59:59Z)
• GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering [69.67264955234494]
  GeoSplatting is a novel approach that augments 3DGS with explicit geometry guidance for precise light transport modeling.<n>By differentiably constructing a surface-grounded 3DGS from an optimizable mesh, our approach leverages well-defined mesh normals and the opaque mesh surface.<n>This enhancement ensures precise material decomposition while preserving the efficiency and high-quality rendering capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-31T17:57:07Z)
• NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images [62.752710734332894]
  NeRSP is a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images. We derive photometric and geometric cues from the polarimetric image formation model and multiview azimuth consistency. We achieve the state-of-the-art surface reconstruction results with only 6 views as input.
  arXiv  Detail & Related papers  (2024-06-11T09:53:18Z)
• 3D Imaging of Complex Specular Surfaces by Fusing Polarimetric and Deflectometric Information [5.729076985389067]
  We introduce a measurement principle that utilizes a novel technique to encode and decode the information contained in a light field reflected off a specular surface. Our approach removes the unrealistic orthographic imaging assumption for SfP, which significantly improves the respective results. We showcase our new technique by demonstrating single-shot and multi-shot measurements on complex-shaped specular surfaces.
  arXiv  Detail & Related papers  (2024-06-04T06:24:07Z)
• Flatten Anything: Unsupervised Neural Surface Parameterization [76.4422287292541]
  We introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization. Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information. Our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies.
  arXiv  Detail & Related papers  (2024-05-23T14:39:52Z)
• 3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks [52.710012864395246]
  We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs. We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
  arXiv  Detail & Related papers  (2021-10-14T23:05:47Z)
• 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)
• Single-molecule orientation localization microscopy I: fundamental limits [0.0]
  We adapt classical and quantum estimation theory and propose a mathematical framework to derive the best possible precision. We find that it is impossible to design an instrument that achieves the maximum sensitivity limit for measuring all possible rotational motions. Overall, we conclude that no single instrument can be optimized for maximum precision across all possible 2D and 3D localization and orientation measurement tasks.
  arXiv  Detail & Related papers  (2020-10-08T15:27:49Z)

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.