The appearance of outdoor scenes changes dramatically with lighting and weather conditions, time of day, and season. Specific conditions, such as the "golden hours" characterized by warm light, can be hard to capture because many scene properties are transient -- they change over time. Despite significant advances in image editing software, common image manipulation tasks such as lighting editing require significant expertise to achieve plausible results.
In this talk, we first explore the appearance of outdoor scenes with an approach based on crowdsourcing and machine learning. We relate visual changes to scene attributes, which are human-nameable concepts used for high-level description of scenes. We collect a dataset containing thousands of outdoor images, annotate them with transient attributes, and train classifiers to recognize these properties in new images. We develop new interfaces for browsing photo collections, based on these attributes.
We then focus on specifically extracting and manipulating the lighting in a photograph. Intrinsic image decomposition separates a photograph into independent layers: reflectance, which represents the color of the materials, and illumination, which encodes the effect of lighting at each pixel. We tackle this ill-posed problem by leveraging additional information provided by multiple photographs of the scene. The methods we describe enable advanced image manipulations such as lighting-aware editing, insertion of virtual objects, and image-based illumination transfer between photographs of a collection.