SpecGen: Neural Spectral BRDF Generation via Spectral-Spatial Tri-plane Aggregation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912745213919232 |
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| author | Jin, Zhenyu Li, Wenjie Ma, Zhanyu Guo, Heng |
| author_facet | Jin, Zhenyu Li, Wenjie Ma, Zhanyu Guo, Heng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17316 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SpecGen: Neural Spectral BRDF Generation via Spectral-Spatial Tri-plane Aggregation Jin, Zhenyu Li, Wenjie Ma, Zhanyu Guo, Heng Computer Vision and Pattern Recognition 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. |
| title | SpecGen: Neural Spectral BRDF Generation via Spectral-Spatial Tri-plane Aggregation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.17316 |