Colorful Diffuse Intrinsic Image Decomposition in the Wild

TL;DR

This paper introduces a method for intrinsic image decomposition that separates diffuse albedo, colorful diffuse shading, and specular residuals, enabling illumination-aware image editing in complex real-world scenes.

Contribution

It proposes a novel extended intrinsic model that relaxes traditional assumptions, allowing for in-the-wild colorful diffuse shading estimation from single images.

Findings

Effective separation of diffuse and specular components in real-world images.

Enables illumination-aware editing like specularity removal and white balancing.

Works despite limited ground-truth datasets.

Abstract

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.