从单张照片中分离出颜色化漫反射光照，支持真实场景图像编辑。
Colorful Diffuse Intrinsic Image Decomposition in the Wild
- 分步去除单一光照与朗伯假设，逐步解出彩色漫反射光照。
- 在仅有少量真实标注数据下，仍可准确估计野外复杂光照。
- 适合需要精准光照分析的图像修复与白平衡应用。
固有图像分解旨在从单张照片中分离表面反照率与光照影响。由于问题复杂，以往方法多假设单一颜色光照和朗伯世界，限制了其在光照感知图像编辑中的应用。本文将输入图像分解为漫反射反照率、彩色漫反射阴影和镜面残差三个成分。通过先去除单一颜色光照，再放宽朗伯世界假设，逐步解决难题。我们证明，将问题拆解为更易处理的子问题后，即使在真实场景标注数据有限的情况下，也能实现对野外彩色漫反射光照的有效估计。所提出的扩展固有模型支持对照片进行光照感知分析，可用于镜面去除、像素级白平衡等图像编辑任务。
原文摘要 · Abstract (English)
Intrinsic image decomposition aims to separate the surface reflectance and the effects from the illumination given a single photograph. Due to the complexity of the problem, most prior works assume a single-color illumination and a Lambertian world, which limits their use in illumination-aware image editing applications. In this work, we separate an input image into its diffuse albedo, colorful diffuse shading, and specular residual components. We arrive at our result by gradually removing first the single-color illumination and then the Lambertian-world assumptions. We show that by dividing the problem into easier sub-problems, in-the-wild colorful diffuse shading estimation can be achieved despite the limited ground-truth datasets. Our extended intrinsic model enables illumination-aware analysis of photographs and can be used for image editing applications such as specularity removal and per-pixel white balancing.
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