We present a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.

我们提出了一种简单而有效的技术来估计单个输入图像中的照明。我们利用在数十亿张标准图像上训练的扩散模型在输入图像中渲染一个铬球，通过研究铬球的外观与初始扩散噪声图之间的关系，我们能够生成高质量的铬球，并通过技术调整使它能够完成HDR照明估计。我们的方法在各种场景下产生令人信服的光照估计结果，并在野外环境中展现出优越的泛化能力。

DiffusionLight: 通过绘制铬球以免费产生光探针