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 https://style-light.github.io.
翻译:我们提出了一个新的照明估计和编辑框架,以便从低动力射程照相机摄取的单一有限视野现场图像(LFOV)中产生高动力距离室内全景照明。现有的照明估计方法要么直接倒退照明代表参数,要么将这一问题分解成LFOV到Panorama和LDR到HDR的照明生成子任务。然而,由于部分观察、高动力射程照明和场景内在的模糊性,照明估计仍是一项具有挑战性的任务。为了解决这一问题,我们提议建立一个双向双向StyleGAN的光学全景合成网络(StyleLightLight),将LDR和HDR全光谱合成集成集成集成成成成一个统一的框架。LDRDR和DRimalimal-LOimalimal-LOral-LOVA的模拟模型模型和LDRM-SB-SLLLLA的模拟模型化集成集成集成的模型,在SDRBAR-BAR-BAR-IAL-IAL-IAL-IAL-IAL-IAL-IAL-ILA的模拟模拟模拟模拟模拟框架中,该模型的模拟模拟模拟模拟模型演示演示方法。