从一张球体RGB图生成可适配任意光照与形状的光谱材质，提升渲染真实感。
SpecGen: Neural Spectral BRDF Generation via Spectral-Spatial Tri-plane Aggregation
- 通过光谱-空间三平面聚合网络建模不同波长和视角下的反射特性。
- 在有限光谱数据下重建准确的光谱BRDF，PSNR提升达8dB。
- 适合需要高保真材质生成的渲染、影视特效与虚拟现实应用。
跨波长合成光谱图像对逼真渲染至关重要。与传统将RGB转换为光谱的方法不同，本文提出SpecGen，仅需一张球体的RGB图像即可生成光谱双向反射分布函数（BRDF），从而实现任意光照和几何形状下的光谱图像渲染。光谱BRDF生成的核心挑战是实测光谱BRDF数据稀缺。为此，我们设计了光谱-空间三平面聚合（SSTA）网络，建模波长与入射-出射方向上的反射响应，利用丰富的RGB BRDF数据来增强光谱生成能力。实验表明，该方法能在有限光谱数据下精准重建光谱BRDF，显著优于现有最先进方法，在超光谱图像重建中实现8 dB的PSNR提升。代码与数据将在录用后公开。
原文摘要 · Abstract (English)
Synthesizing spectral images across different wavelengths is essential for photorealistic rendering. Unlike conventional spectral uplifting methods that convert RGB images into spectral ones, we introduce SpecGen, a novel method that generates spectral bidirectional reflectance distribution functions (BRDFs) from a single RGB image of a sphere. This enables spectral image rendering under arbitrary illuminations and shapes covered by the corresponding material. A key challenge in spectral BRDF generation is the scarcity of measured spectral BRDF data. To address this, we propose the Spectral-Spatial Tri-plane Aggregation (SSTA) network, which models reflectance responses across wavelengths and incident-outgoing directions, allowing the training strategy to leverage abundant RGB BRDF data to enhance spectral BRDF generation. Experiments show that our method accurately reconstructs spectral BRDFs from limited spectral data and surpasses state-of-the-art methods in hyperspectral image reconstruction, achieving an improvement of 8 dB in PSNR. Codes and data will be released upon acceptance.
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