野外多彩漫反射内在图像分解

Colorful Diffuse Intrinsic Image Decomposition in the Wild

September 20, 2024

作者: Chris Careaga, Yağız Aksoy

cs.AI

摘要

内在图像分解旨在在给定单张照片的情况下分离表面反射和光照效果。由于问题的复杂性，大多数先前的研究假设单色光照和朗伯世界，这限制了它们在光照感知图像编辑应用中的使用。在这项工作中，我们将输入图像分解为漫反射反照率、多彩漫反射阴影和镜面残差组件。我们通过逐步消除首先是单色光照，然后是朗伯世界的假设来得出我们的结果。我们表明，通过将问题分解为更容易的子问题，尽管受限于有限的真实数据集，可以实现野外多彩漫反射阴影估计。我们扩展的内在模型实现了对照片的光照感知分析，并可用于图像编辑应用，如去除镜面反射和逐像素白平衡。

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.