让天空模型更真实，直接从真实高动态图像学习天气变化。
Towards Physically-Based Sky-Modeling
- 从真实拍摄的HDR图像中学习天气化天空，直接生成物理一致的环境图。
- 支持用户控制太阳位置和云层分布，保持14EV扩展动态范围。
- 适合需要真实光照的影视渲染、虚拟现实和科学模拟场景。
准确的环境图是实现户外场景逼真渲染与一致光照的关键，广泛应用于视觉艺术、沉浸式虚拟现实及工程科学领域。尽管现有天空模型已涵盖云层等要素，但其生成的环境图在色调、阴影和光照一致性上仍无法忠实还原真实拍摄的高动态范围图像（HDRI）。我们发现，当前基于深度神经网络生成的低/高动态范围图像虽质量提升显著，但与天空建模的核心目标无关。由于包含太阳的室外环境图需14EV扩展动态范围，传统高动态范围成像（HDRI）范式已不足以支撑天空建模。本文提出全天气天空模型（AllSky），直接从真实捕捉的HDR图像中学习天气化天空，支持用户控制太阳位置与云层布局，有效保留天空的14EV扩展动态范围，实现更接近真实物理环境的光照重现。
原文摘要 · Abstract (English)
Accurate environment maps are a key component in rendering photorealistic outdoor scenes with coherent illumination. They enable captivating visual arts, immersive virtual reality and a wide range of engineering and scientific applications. Recent works have extended sky-models to be more comprehensive and inclusive of cloud formations but existing approaches fall short in faithfully recreating key-characteristics in physically captured HDRI. As we demonstrate, environment maps produced by sky-models do not relight scenes with the same tones, shadows, and illumination coherence as physically captured HDR imagery. Though the visual quality of DNN-generated LDR and HDR imagery has greatly progressed in recent years, we demonstrate this progress to be tangential to sky-modelling. Due to the Extended Dynamic Range (EDR) of 14EV required for outdoor environment maps inclusive of the sun, sky-modelling extends beyond the conventional paradigm of High Dynamic Range Imagery (HDRI). In this work, we propose an all-weather sky-model, learning weathered-skies directly from physically captured HDR imagery. Per user-controlled positioning of the sun and cloud formations, our model (AllSky) allows for emulation of physically captured environment maps with improved retention of the Extended Dynamic Range (EDR) of the sky.
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