Thermal Polarimetric Multi-view Stereo

• URL: http://arxiv.org/abs/2510.20972v1
• Date: Thu, 23 Oct 2025 20:00:41 GMT
• Title: Thermal Polarimetric Multi-view Stereo
• Authors: Takahiro Kushida, Kenichiro Tanaka,
• Abstract summary: We formulate a general theory of polarization observation and show that long-wave infrared (LWIR) polarimetric imaging is free from the ambiguities that affect visible polarization analyses.<n>We propose a method for recovering detailed 3D shapes using multi-view thermal polarimetric images.
• Score: 3.837999763042303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel method for detailed 3D shape reconstruction utilizing thermal polarization cues. Unlike state-of-the-art methods, the proposed approach is independent of illumination and material properties. In this paper, we formulate a general theory of polarization observation and show that long-wave infrared (LWIR) polarimetric imaging is free from the ambiguities that affect visible polarization analyses. Subsequently, we propose a method for recovering detailed 3D shapes using multi-view thermal polarimetric images. Experimental results demonstrate that our approach effectively reconstructs fine details in transparent, translucent, and heterogeneous objects, outperforming existing techniques.

Related papers

• PolarAnything: Diffusion-based Polarimetric Image Synthesis [59.14294818211059]
  We propose PolarAnything, capable of synthesizing polarization images from a single RGB input with both photorealism and physical accuracy.<n>Experiments show that our model generates high-quality polarization images and supports downstream tasks like shape from polarization.
  arXiv  Detail & Related papers  (2025-07-23T07:09:10Z)
• Shape from Polarization of Thermal Emission and Reflection [2.7317088388886384]
  We leverage the Shape from Polarization (SfP) technique in the Long-Wave Infrared (LWIR) spectrum, where most materials are opaque and emissive.<n>We formulated a polarization model that explicitly accounts for the combined effects of emission and reflection.<n>We implemented a prototype system and created ThermoPol, the first real-world benchmark dataset for LWIR SfP.
  arXiv  Detail & Related papers  (2025-06-23T00:33:17Z)
• 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)
• S2P3: Self-Supervised Polarimetric Pose Prediction [55.43547228561919]
  This paper proposes the first self-supervised 6D object pose prediction from multimodal RGB+polarimetric images. The novel training paradigm comprises 1) a physical model to extract geometric information of polarized light, 2) a teacher-student knowledge distillation scheme and 3) a self-supervised loss formulation through differentiable constraints.
  arXiv  Detail & Related papers  (2023-12-02T10:46:40Z)
• NeISF: Neural Incident Stokes Field for Geometry and Material Estimation [50.588983686271284]
  Multi-view inverse rendering is the problem of estimating the scene parameters such as shapes, materials, or illuminations from a sequence of images captured under different viewpoints. We propose Neural Incident Stokes Fields (NeISF), a multi-view inverse framework that reduces ambiguities using polarization cues.
  arXiv  Detail & Related papers  (2023-11-22T06:28:30Z)
• PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection [81.16859686137435]
  We present PARTNER, a novel 3D object detector in the polar coordinate. Our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on and ONCE validation set.
  arXiv  Detail & Related papers  (2023-08-08T01:59:20Z)
• Polarimetric Multi-View Inverse Rendering [13.391866136230165]
  A polarization camera has great potential for 3D reconstruction since the angle of polarization (AoP) and the degree of polarization (DoP) of reflected light are related to an object's surface normal. We propose a novel 3D reconstruction method called Polarimetric Multi-View Inverse Rendering (Polarimetric MVIR) that effectively exploits geometric, photometric, and polarimetric cues extracted from input multi-view color-polarization images.
  arXiv  Detail & Related papers  (2022-12-24T12:12:12Z)
• Transparent Shape from a Single View Polarization Image [6.18278691318801]
  This paper presents a learning-based method for transparent surface estimation from a single view polarization image. Existing shape from polarization(SfP) methods have the difficulty in estimating transparent shape since the inherent transmission interference heavily reduces the reliability of physics-based prior.
  arXiv  Detail & Related papers  (2022-04-13T12:24:32Z)
• 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)
• Deep Polarization Imaging for 3D shape and SVBRDF Acquisition [7.86578678811226]
  We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues. Unlike previous works that have exploited polarization to estimate material or object appearance under certain constraints, we lift such restrictions by coupling polarization imaging with deep learning. We demonstrate our approach to achieve superior results compared to recent works employing deep learning in conjunction with flash illumination.
  arXiv  Detail & Related papers  (2021-05-06T17:58:43Z)
• Polarimetric Multi-View Inverse Rendering [13.391866136230165]
  A polarization camera has great potential for 3D reconstruction since the angle of polarization (AoP) of reflected light is related to an object's surface normal. We propose a novel 3D reconstruction method called Polarimetric Multi-View Inverse Rendering (Polarimetric MVIR) that exploits geometric, photometric, and polarimetric cues extracted from input multi-view color polarization images.
  arXiv  Detail & Related papers  (2020-07-17T09:00:20Z)

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.