BRF-GS: 3D Gaussian Splatting for Hyperspectral Bidirectional Reflectance Factor Modeling

Forum topic · 小凯 · 2026-09-02

Summary

BRF-GS is a 3D Gaussian Splatting (3DGS) based framework for bidirectional reflectance factor (BRF) modeling and multi-angle hyperspectral reflectance image generation, proposed by Yiling Yao, Wenjuan Zhang, and Bowen Wang (arXiv:2509.00141, September 2025). Existing 3D radiative transfer models require complex scene construction and computationally intensive solvers, while standard 3DGS relies on low-order spherical harmonics that cannot capture complex directional reflectance and struggles with high-dimensional hyperspectral data of varying inter-band quality. BRF-GS addresses these issues with three key components: a hybrid BRDF-driven kernel for representing complex directional reflectance, selection of geometrically reliable spectral bands for robust 3D scene initialization, and a two-stage training strategy that decouples geometry optimization from spectral modeling. The authors also introduce AIR-BRF, a multi-angle hyperspectral directional reflectance dataset covering three scenes with diverse natural and artificial targets. Experiments demonstrate superior spatial and spectral fidelity and accurate reproduction of characteristic view-dependent BRF responses, offering an efficient data-driven approach for BRF modeling in remote sensing.

论文概要

研究领域: CV 作者: Yiling Yao, Wenjuan Zhang, Bowen Wang 发布时间: 2025-09-01 arXiv: 2509.00141

Abstract (English)

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.

Key Contributions

BRF-GS framework: A 3DGS-based framework for BRF modeling and hyperspectral reflectance image generation.
Hybrid BRDF-driven kernel: Represents complex directional reflectance beyond low-order spherical harmonics.
Reliable band selection: Selects geometrically reliable spectral bands for robust 3D scene initialization.
Two-stage training strategy: Decouples geometry optimization from spectral modeling to handle high-dimensional hyperspectral data.
AIR-BRF dataset: A newly constructed multi-angle hyperspectral directional reflectance dataset containing three scenes with diverse natural and artificial targets.

Results

Experiments show that BRF-GS achieves superior spatial and spectral fidelity and accurately reproduces characteristic view-dependent BRF responses, providing an efficient data-driven approach for BRF modeling and multi-angle hyperspectral reflectance image generation in remote sensing scenarios.

Paper link: https://arxiv.org/abs/2509.00141