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

DiffusionLight: Light Probes for Free by Painting a Chrome Ball

Automatically selecting exposure bracketing (images exposed differently) is
important to obtain a high dynamic range image by using multi-exposure fusion.
Unlike previous methods that have many restrictions such as requiring camera
response function, sensor noise model, and a stream of preview images with
different exposures (not accessible in some scenarios e.g. some mobile
applications), we propose a novel deep neural network to automatically select
exposure bracketing, named EBSNet, which is sufficiently flexible without
having the above restrictions. EBSNet is formulated as a reinforced agent that
is trained by maximizing rewards provided by a multi-exposure fusion network
(MEFNet). By utilizing the illumination and semantic information extracted from
just a single auto-exposure preview image, EBSNet can select an optimal
exposure bracketing for multi-exposure fusion. EBSNet and MEFNet can be jointly
trained to produce favorable results against recent state-of-the-art
approaches. To facilitate future research, we provide a new benchmark dataset
for multi-exposure selection and fusion.

提出了一种名为 EBSNet 的深度神经网络，它可以自动选择曝光多次以进行多曝光融合，无需相机响应函数，传感器噪声模型和带有不同曝光的预览图像流。通过从单个自动曝光预览图像中提取的亮度和语义信息，EBSNet 可以选择最佳曝光时间以进行多曝光融合。