BRF-GS: Hyperspectral Bidirectional Reflectance Factor Modeling and Image Generation Based on 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2608.31159v1
• Date: Mon, 31 Aug 2026 17:56:57 GMT
• Title: BRF-GS: Hyperspectral Bidirectional Reflectance Factor Modeling and Image Generation Based on 3D Gaussian Splatting
• Abstract summary: bidirectional reflectance factor (BRF) characterizes the directional radiative properties of terrestrial surfaces.<n>Existing 3D radiative transfer models require complex scene construction and computationally intensive radiative transfer solvers.<n>We propose BRF-GS, a 3DGS-based framework for BRF modeling and hyperspectral reflectance image generation.
• Score: 16.27199512129977
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The bidirectional reflectance factor (BRF) characterizes the directional radiative properties of terrestrial surfaces. However, existing three-dimensional (3D) radiative transfer models require complex scene construction and computationally intensive radiative transfer solvers, limiting efficient generation of multi-angle hyperspectral reflectance imagery. 3D Gaussian Splatting (3DGS) offers an efficient framework for neural scene representation and novel view synthesis, but its low-order spherical harmonics representation is insufficient for complex directional reflectance, while the high dimensionality and inter-band quality differences of hyperspectral data introduce additional challenges. To address these challenges, we propose BRF-GS, a 3DGS-based framework for BRF modeling and hyperspectral reflectance image generation. BRF-GS introduces a hybrid BRDF-driven kernel to represent complex directional reflectance, selects geometry-reliable spectral bands for robust 3D scene initialization, and adopts a two-stage training strategy that decouples geometry optimization from spectral modeling. We further construct the AIR-BRF dataset, a multi-angle hyperspectral directional reflectance dataset comprising three scenes with diverse natural and artificial targets. Experiments demonstrate that BRF-GS achieves superior spatial and spectral fidelity and accurately reproduces characteristic view-dependent BRF responses. The proposed framework provides an efficient data-driven approach for BRF modeling and multi-angle hyperspectral reflectance image generation in remote sensing scenes.

Related papers

• CORF-GS: Real-Time Wireless Radiance Field Reconstruction via Coupled Optical-RF Gaussian Splatting [65.63543177449922]
  CORF-GS is a real-time WRF reconstruction framework that processes sequential and radio frequency (RF) waves.<n>It constructs a unified Gaussian representation for optical and RF with shared geometry and modality-specific appearance.<n>It achieves state-of-the-art RF spectrum quality and reduces the reconstruction time by $6.4times$ compared with existing WRF methods.
  arXiv  Detail & Related papers  (2026-07-28T10:56:52Z)
• Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis [13.057153756597904]
  We introduce a framework enabling differentiable RF propagation simulation directly within visually reconstructed neural scenes.<n>We provide cross-modal evidence that neural reconstructions can serve as unified spatial representations for both electromagnetic propagation simulation and photorealistic view synthesis.
  arXiv  Detail & Related papers  (2026-05-08T14:19:28Z)
• PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction [76.9760302941819]
  PolGS++ is a polarimetric Gaussian Splatting framework for fast reflective surface reconstruction.<n>We introduce a depth-guided visibility mask acquisition mechanism that enables angle-of-polarization (AoP)-based tangent-space consistency constraints.
  arXiv  Detail & Related papers  (2026-03-11T14:11:42Z)
• Ref-DGS: Reflective Dual Gaussian Splatting [51.77181103979343]
  Reflective appearance, especially strong and typically near-field specular reflections, poses a fundamental challenge for accurate surface reconstruction and novel view.<n>We present Ref-DGS, a reflective dual Gaussian splatting framework that addresses this trade-off.<n>Experiments demonstrate that Ref-DGS achieves state-of-the-art performance on reflective scenes while training substantially faster than ray-based Gaussian methods.
  arXiv  Detail & Related papers  (2026-03-08T14:54:15Z)
• TR-Gaussians: High-fidelity Real-time Rendering of Planar Transmission and Reflection with 3D Gaussian Splatting [35.344270353035945]
  TR-Gaussians is a novel 3D-Gaussian-based representation for high-fidelity rendering of planar transmission and reflection.<n>Experiments on different datasets demonstrate that TR-Gaussians achieve real-time, high-fidelity novel view synthesis.
  arXiv  Detail & Related papers  (2025-11-17T06:09:21Z)
• RTR-GS: 3D Gaussian Splatting for Inverse Rendering with Radiance Transfer and Reflection [16.81533668816093]
  RTR-GS is a novel inverse rendering framework capable of robustly rendering objects with arbitrary reflectance properties, decomposing BRDF and lighting, and delivering credible relighting results.<n>We show that our method enhances novel view synthesis, normal estimation, decomposition, and relighting while maintaining efficient training inference process.
  arXiv  Detail & Related papers  (2025-07-10T13:13:08Z)
• 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)
• Diffusion Denoised Hyperspectral Gaussian Splatting [11.486860334986394]
  3D reconstruction methods have been used to create implicit neural representations of hyperspectral scenes.<n>We propose Diffusion-Denoised Hyperspectral Gaussian Splatting (DD-HGS) to enable 3D explicit reconstruction of hyperspectral scenes.
  arXiv  Detail & Related papers  (2025-05-28T02:07:52Z)
• BRDF-NeRF: Neural Radiance Fields with Optical Satellite Images and BRDF Modelling [0.0]
  We introduce BRDF-NeRF, which incorporates the physically-based semi-empirical Rahman-Pinty-Verstraete (RPV) BRDF model. BRDF-NeRF successfully synthesizes novel views from unseen angles and generates high-quality digital surface models.
  arXiv  Detail & Related papers  (2024-09-18T14:28:52Z)
• Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation [51.346733271166926]
  Mesh2NeRF is an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. We validate the effectiveness of Mesh2NeRF across various tasks.
  arXiv  Detail & Related papers  (2024-03-28T11:22:53Z)
• Anti-Aliased Neural Implicit Surfaces with Encoding Level of Detail [54.03399077258403]
  We present LoD-NeuS, an efficient neural representation for high-frequency geometry detail recovery and anti-aliased novel view rendering. Our representation aggregates space features from a multi-convolved featurization within a conical frustum along a ray.
  arXiv  Detail & Related papers  (2023-09-19T05:44:00Z)

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.