We present a new lighting estimation and editing framework to generate
high-dynamic-range (HDR) indoor panorama lighting from a single limited
field-of-view (LFOV) image captured by low-dynamic-range (LDR) cameras.
Existing lighting estimation methods either directly regress lighting
representation parameters or decompose this problem into LFOV-to-panorama and
LDR-to-HDR lighting generation sub-tasks. However, due to the partial
observation, the high-dynamic-range lighting, and the intrinsic ambiguity of a
scene, lighting estimation remains a challenging task. To tackle this problem,
we propose a coupled dual-StyleGAN panorama synthesis network (StyleLight) that
integrates LDR and HDR panorama synthesis into a unified framework. The LDR and
HDR panorama synthesis share a similar generator but have separate
discriminators. During inference, given an LDR LFOV image, we propose a
focal-masked GAN inversion method to find its latent code by the LDR panorama
synthesis branch and then synthesize the HDR panorama by the HDR panorama
synthesis branch. StyleLight takes LFOV-to-panorama and LDR-to-HDR lighting
generation into a unified framework and thus greatly improves lighting
estimation. Extensive experiments demonstrate that our framework achieves
superior performance over state-of-the-art methods on indoor lighting
estimation. Notably, StyleLight also enables intuitive lighting editing on
indoor HDR panoramas, which is suitable for real-world applications. Code is
available at this https URL

用于室内全景高动态范围 HDR 照明估计和编辑的新光照估计和编辑框架，从一张低动态范围 LDR 相机拍摄的单个有限视场 LFOV 图像生成高动态范围 (HDR) 的室内全景照明。该框架结合了 LDR 和 HDR 全景合成，使用了 LFOV - 全景和 LDR-to-HDR 光照生成方法，提出了一种耦合双 StyleGAN 全景合成网络 (StyleLight)，并且在室内 HDR 全景照明估计方面表现出优秀的性能。

StyleLight: 用于光照估计和编辑的 HDR 全景图生成

StyleLight: HDR Panorama Generation for Lighting Estimation and Editing

High dynamic range (HDR) imaging provides the capability of handling real
world lighting as opposed to the traditional low dynamic range (LDR) which
struggles to accurately represent images with higher dynamic range. However,
most imaging content is still available only in LDR. This paper presents a
method for generating HDR content from LDR content based on deep Convolutional
Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input
and generates images with an expanded range in an end-to-end fashion. The model
attempts to reconstruct missing information that was lost from the original
signal due to quantization, clipping, tone mapping or gamma correction. The
added information is reconstructed from learned features, as the network is
trained in a supervised fashion using a dataset of HDR images. The approach is
fully automatic and data driven; it does not require any heuristics or human
expertise. ExpandNet uses a multiscale architecture which avoids the use of
upsampling layers to improve image quality. The method performs well compared
to expansion/inverse tone mapping operators quantitatively on multiple metrics,
even for badly exposed inputs.

本研究提出了一种基于深度卷积神经网络的 HDR 图像恢复方法：ExpandNet，该方法可以自动地将低动态范围（LDR）的图像转换为高动态范围（HDR）的图像，通过学习特征来重构由于量化、截取、色调映射或 gamma 校正而丢失的信息，同时通过避免上采样层来提高图像质量，在多个指标上表现良好。