野外多彩散射內在影像分解

Colorful Diffuse Intrinsic Image Decomposition in the Wild

September 20, 2024

作者: Chris Careaga, Yağız Aksoy

cs.AI

摘要

內在影像分解的目標是在給定單張照片的情況下，分離出表面反射率和光線照射效果。由於問題的複雜性，大多數先前的研究假設單色光照和蘭伯特世界，這限制了它們在具有照明感知的圖像編輯應用中的使用。在這項研究中，我們將輸入圖像分離為其漫反射反照率、色彩漫反射陰影和鏡面殘留組件。我們通過逐步消除首先是單色光照，然後是蘭伯特世界的假設，得出我們的結果。我們展示通過將問題分解為更容易的子問題，盡管受限於有限的真實數據集，也可以實現野外多彩漫反射陰影的估計。我們擴展的內在模型使得能夠對照片進行照明感知分析，並可用於圖像編輯應用，如去除鏡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.