Intrinsic Image Decomposition from Multiple Photographs 2012 - 136 pages Editing materials and lighting is a common image manipulation task that requires significant expertise to achieve plausible results. Each pixel aggregates the effect of both material and lighting, therefore standard color manipulations are likely to affect both components. 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. In this thesis, we tackle this ill-posed problem by leveraging additional information provided by multiple photographs of the scene. We combine image-guided algorithms with sparse 3D information reconstructed from multi-view stereo, in order to constrain the decomposition. We first present an approach to decompose images of outdoor scenes, using photographs captured at a single time of day. This method not only separates reflectance from illumination, but also decomposes the illumination into sun, sky and indirect layers. We then develop a new method to extract lighting information about a scene only from a few images, thus simplifying the capture and calibration steps of our intrinsic decomposition. In the third part of this thesis, we focus on image collections gathered from photo-sharing websites or captured with a moving light source. We exploit the variations of lighting to process complex scenes without user assistance, not precise and complete geometry. The method described in this thesis enable advanced image manipulations such as lighting-aware editing, insertion of virtual objects, and image-based illumination transfer between photographs of a collection. |
