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。