PolarGuide-GSDR: 3D Gaussian Splatting Driven by Polarization Priors and Deferred Reflection for Real-World Reflective Scenes

• URL: http://arxiv.org/abs/2512.02664v1
• Date: Tue, 02 Dec 2025 11:34:55 GMT
• Title: PolarGuide-GSDR: 3D Gaussian Splatting Driven by Polarization Priors and Deferred Reflection for Real-World Reflective Scenes
• Authors: Derui Shan, Qian Qiao, Hao Lu, Tao Du, Peng Lu,
• Abstract summary: Polarization-forward-guided paradigm establishing a bidirectional coupling mechanism between polarization and 3DGS.<n>We demonstrate that PolarGuide-GSDR achieves state-of-the-art performance in specular reconstruction, normal estimation, and novel view synthesis.
• Score: 14.263443353159978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Polarization-aware Neural Radiance Fields (NeRF) enable novel view synthesis of specular-reflection scenes but face challenges in slow training, inefficient rendering, and strong dependencies on material/viewpoint assumptions. However, 3D Gaussian Splatting (3DGS) enables real-time rendering yet struggles with accurate reflection reconstruction from reflection-geometry entanglement, adding a deferred reflection module introduces environment map dependence. We address these limitations by proposing PolarGuide-GSDR, a polarization-forward-guided paradigm establishing a bidirectional coupling mechanism between polarization and 3DGS: first 3DGS's geometric priors are leveraged to resolve polarization ambiguity, and then the refined polarization information cues are used to guide 3DGS's normal and spherical harmonic representation. This process achieves high-fidelity reflection separation and full-scene reconstruction without requiring environment maps or restrictive material assumptions. We demonstrate on public and self-collected datasets that PolarGuide-GSDR achieves state-of-the-art performance in specular reconstruction, normal estimation, and novel view synthesis, all while maintaining real-time rendering capabilities. To our knowledge, this is the first framework embedding polarization priors directly into 3DGS optimization, yielding superior interpretability and real-time performance for modeling complex reflective scenes.

Related papers

• SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction [7.2438370491008754]
  We propose SSR-GS, a specular reflection modeling framework for glossy surface reconstruction.<n>Specifically, we introduce a prefiltered Mip-Cubemap to model direct specular reflections efficiently, and propose an IndiASG module to capture indirect specular reflections.<n>Experiments on both synthetic and real-world datasets demonstrate that SSR-GS achieves state-of-the-art performance in glossy surface reconstruction.
  arXiv  Detail & Related papers  (2026-03-05T13:24:13Z)
• PolGS: Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction [68.8705801813427]
  PolGS is a Polarimetric Gaussian Splatting model allowing fast reflective surface reconstruction in 10 minutes.<n>By integrating polarimetric constraints into the 3DGS framework, PolGS effectively separates specular and diffuse components, enhancing reconstruction quality for challenging reflective materials.
  arXiv  Detail & Related papers  (2025-09-24T03:09:52Z)
• Reflections Unlock: Geometry-Aware Reflection Disentanglement in 3D Gaussian Splatting for Photorealistic Scenes Rendering [51.223347330075576]
  Ref-Unlock is a novel geometry-aware reflection modeling framework based on 3D Gaussian Splatting.<n>Our approach employs a dual-branch representation with high-order spherical harmonics to capture high-frequency reflective details.<n>Our method thus offers an efficient and generalizable solution for realistic rendering of reflective scenes.
  arXiv  Detail & Related papers  (2025-07-08T15:45:08Z)
• Generalizable and Relightable Gaussian Splatting for Human Novel View Synthesis [49.67420486373202]
  GRGS is a generalizable and relightable 3D Gaussian framework for high-fidelity human novel view synthesis under diverse lighting conditions.<n>We introduce a Lighting-aware Geometry Refinement (LGR) module trained on synthetically relit data to predict accurate depth and surface normals.
  arXiv  Detail & Related papers  (2025-05-27T17:59:47Z)
• 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)
• GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering [83.69136534797686]
  We present GUS-IR, a novel framework designed to address the inverse rendering problem for complicated scenes featuring rough and glossy surfaces. This paper starts by analyzing and comparing two prominent shading techniques popularly used for inverse rendering, forward shading and deferred shading. We propose a unified shading solution that combines the advantages of both techniques for better decomposition.
  arXiv  Detail & Related papers  (2024-11-12T01:51:05Z)
• 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)
• RefGaussian: Disentangling Reflections from 3D Gaussian Splatting for Realistic Rendering [18.427759763663047]
  We propose RefGaussian to disentangle reflections from 3D-GS for realistically modeling reflections. We employ local regularization techniques to ensure local smoothness for both the transmitted and reflected components. Our approach achieves superior novel view synthesis and accurate depth estimation outcomes.
  arXiv  Detail & Related papers  (2024-06-09T16:49:39Z)
• GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors [8.8400072344375]
  Learning surfaces from neural radiance field (NeRF) became a rising topic in Multi-View Stereo (MVS) Recent methods demonstrated their ability to reconstruct accurate 3D shapes of Lambertian scenes. However, their results on reflective scenes are unsatisfactory due to the entanglement of specular radiance and complicated geometry.
  arXiv  Detail & Related papers  (2024-03-18T15:58:03Z)
• GS-IR: 3D Gaussian Splatting for Inverse Rendering [71.14234327414086]
  We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (GS) We extend GS, a top-performance representation for novel view synthesis, to estimate scene geometry, surface material, and environment illumination from multi-view images captured under unknown lighting conditions. The flexible and expressive GS representation allows us to achieve fast and compact geometry reconstruction, photorealistic novel view synthesis, and effective physically-based rendering.
  arXiv  Detail & Related papers  (2023-11-26T02:35:09Z)

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.