扩散Light-Turbo:通过单通 Chrome 球 Inpainting 免费加速光探测器
IEEE transactions on pattern analysis and machine intelligence
|February 2, 2026
概括
我们开发了DiffusionLight-Turbo,这是一个快速的人工智能方法,用于从单个图像中估计照明. 它使用扩散模型来产生真实的照明,以最小的质量损失实现60倍的加速度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 计算机图形 计算机图形
背景情况:
- 从低动态范围 (LDR) 图像中估计照明对于实现现实的染至关重要.
- 由于有限的高动态范围 (HDR) 全景数据集,现有的方法难以通用.
- 扩散模型具有潜力,但在内容一致性和HDR输出方面面临挑战.
研究的目的:
- 引入一种高效和有效的技术,用于从单个LDR图像中估计照明.
- 为了克服以前的照明估计方法的概括限制.
- 为了显著减少照明估计的计算时间.
主要方法:
- 使用稳定扩散XL.重构的照明估计作为一个球inpainting问题使用稳定扩散XL.
- 引入了 DiffusionLight,采用代 inpainting 和中间集成用于稳定的照明先验.
- 开发了具有Turbo LoRA和LoRA交换的DiffusionLight-Turbo,以减少60倍的运行时间 (约. 30秒) 的时间.
- 微调了一个曝光LoRA,用于从多个LDR图像中生成HDR光探测器.
主要成果:
- DiffusionLight-Turbo比 DiffusionLight 实现了 60 倍的加速,将估计时间缩短到 ~ 30 秒,质量降低最小.
- 该方法在各种环境中产生了令人信服的光线估计.
- 与现有技术相比,在野外场景中表现出优越的概括能力.
结论:
- DiffusionLight-Turbo为单图像照明估计提供了一个高效和有效的解决方案.
- 该技术利用扩散模型来实现强大的性能和通用化.
- 该代码是公开的,有助于进一步的研究和应用.
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