Segmentation-Driven Monocular Shape from Polarization based on Physical Model

• URL: http://arxiv.org/abs/2601.04776v1
• Date: Thu, 08 Jan 2026 09:57:47 GMT
• Title: Segmentation-Driven Monocular Shape from Polarization based on Physical Model
• Authors: Jinyu Zhang, Xu Ma, Weili Chen, Gonzalo R. Arce,
• Abstract summary: This paper introduces a novel segmentation-driven monocular SfP framework.<n>It reformulates global shape recovery into a set of local reconstructions over adaptively segmented convex sub-regions.<n>Experiments on both synthetic and real-world datasets validate the proposed approach.
• Score: 15.846916365035854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monocular shape-from-polarization (SfP) leverages the intrinsic relationship between light polarization properties and surface geometry to recover surface normals from single-view polarized images, providing a compact and robust approach for three-dimensional (3D) reconstruction. Despite its potential, existing monocular SfP methods suffer from azimuth angle ambiguity, an inherent limitation of polarization analysis, that severely compromises reconstruction accuracy and stability. This paper introduces a novel segmentation-driven monocular SfP (SMSfP) framework that reformulates global shape recovery into a set of local reconstructions over adaptively segmented convex sub-regions. Specifically, a polarization-aided adaptive region growing (PARG) segmentation strategy is proposed to decompose the global convexity assumption into locally convex regions, effectively suppressing azimuth ambiguities and preserving surface continuity. Furthermore, a multi-scale fusion convexity prior (MFCP) constraint is developed to ensure local surface consistency and enhance the recovery of fine textural and structural details. Extensive experiments on both synthetic and real-world datasets validate the proposed approach, showing significant improvements in disambiguation accuracy and geometric fidelity compared with existing physics-based monocular SfP techniques.

Related papers

• Seamlessly Natural: Image Stitching with Natural Appearance Preservation [0.6089774484591287]
  SENA prioritizes structural fidelity in challenging real-world scenes characterized by parallax and depth variation.<n> SENA addresses fundamental limitations through three key contributions.<n>Experiments conducted on challenging datasets demonstrate that SENA achieves alignment accuracy comparable to leading homography-based methods.
  arXiv  Detail & Related papers  (2026-01-03T18:40:35Z)
• RefLSM: Linearized Structural-Prior Reflectance Model for Medical Image Segmentation and Bias-Field Correction [10.716406019360441]
  We propose a novel variational Reflectance-based Level Set Model (RefLSM) for medical image segmentation.<n>RefLSM explicitly integrates Retinex-inspired reflectance decomposition into the segmentation framework.<n>We show that RefLSM achieves superior segmentation accuracy, robustness, and computational efficiency compared to state-of-the-art level set methods.
  arXiv  Detail & Related papers  (2025-12-08T06:06:29Z)
• 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)
• HiNeuS: High-fidelity Neural Surface Mitigating Low-texture and Reflective Ambiguity [8.74691272469226]
  HiNeuS is a unified framework that holistically addresses three core limitations in existing approaches.<n>We introduce: 1) Differential visibility verification through SDF-guided ray tracing; 2) Planar-conformal regularization via ray-aligned geometry patches; and 3) Physically-grounded Eikonal relaxation that dynamically modulates geometric constraints based on local gradients.
  arXiv  Detail & Related papers  (2025-06-30T13:45:25Z)
• Towards Explicit Geometry-Reflectance Collaboration for Generalized LiDAR Segmentation in Adverse Weather [58.4718010073085]
  Existing LiDAR segmentation models often suffer from decreased accuracy when exposed to adverse weather conditions.<n>Recent methods addressing this issue focus on enhancing training data through weather simulation or universal augmentation techniques.<n>We propose a novel Geometry-Reflectance Collaboration framework that explicitly separates feature extraction for geometry and reflectance.
  arXiv  Detail & Related papers  (2025-06-03T03:23:43Z)
• Single-Shot Shape and Reflectance with Spatial Polarization Multiplexing [20.968382043334632]
  We propose spatial polarization multiplexing (SPM) for reconstructing object shape and reflectance from a single polarimetric image.<n>Unlike single-pattern structured light with intensity and color, our polarization pattern is invisible to the naked eye and retains the natural surface appearance.
  arXiv  Detail & Related papers  (2025-04-17T17:59:50Z)
• ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction [50.07671826433922]
  It is non-trivial to simultaneously recover meticulous geometry and preserve smoothness across regions with differing characteristics.<n>We propose ND-SDF, which learns a Normal Deflection field to represent the angular deviation between the scene normal and the prior normal.<n>Our method not only obtains smooth weakly textured regions such as walls and floors but also preserves the geometric details of complex structures.
  arXiv  Detail & Related papers  (2024-08-22T17:59:01Z)
• SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization [21.377923666134116]
  Shape from Polarization (SfP) is the problem popularly known as Shape from Polarization (SfP) We present a novel inverse rendering-based framework to estimate the 3D shape (per-pixel surface normals and depth) of objects and scenes from single-view polarization images.
  arXiv  Detail & Related papers  (2024-07-12T14:29:00Z)
• RaNeuS: Ray-adaptive Neural Surface Reconstruction [87.20343320266215]
  We leverage a differentiable radiance field eg NeRF to reconstruct detailed 3D surfaces in addition to producing novel view renderings. Considering that different methods formulate and optimize the projection from SDF to radiance field with a globally constant Eikonal regularization, we improve with a ray-wise weighting factor. Our proposed textitRaNeuS are extensively evaluated on both synthetic and real datasets.
  arXiv  Detail & Related papers  (2024-06-14T07:54:25Z)
• 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)
• Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization [99.96632216070718]
  Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution. The high-dimensional spatiality characteristic and complex geometrical structure of LF images make the problem more challenging than traditional single-image SR. We propose a novel learning-based LF framework, in which each view of an LF image is first individually super-resolved.
  arXiv  Detail & Related papers  (2020-04-05T14:39:57Z)

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.