Data-driven modeling of outdoor illumination
Jean-Francois Lalonde - University of Laval
Oct. 7, 2015, 1 p.m. - Oct. 7, 2015, 2:30 p.m.
McConnell Bldg. Rm 437
Outdoor illumination creates challenges for computer vision and graphics alike. In vision, algorithms routinely get confused by strong shadows, highlights, and glare. In graphics, simulating the extreme dynamic range of outdoor lighting needs to be done accurately to realistically synthesize these effects. In this talk, I will present approaches that aim to improve our understanding of natural lighting with applications in both vision and graphics. In particular, I will present recent work that relies on a data-driven model, trained on a novel dataset of 8,000+ HDR photographs of daytime skies. We leverage this new dataset to 1) automatically estimate the illumination conditions in image collections, which allows us to seamlessly insert virtual objects in the images, and 2) characterize the behavior of photometric stereo under natural lighting.
BIOGRAPHY
Jean-François Lalonde is an assistant professor in ECE at Laval University, Quebec City. Previously, he was a Post-Doctoral Associate at Disney Research, Pittsburgh. He received a B.Eng. degree in Computer Engineering with honors from Laval University, Canada, in 2004. He earned his M.S at the Robotics Institute at Carnegie Mellon University in 2006 and received his Ph.D., also from Carnegie Mellon, in 2011. After graduation, he became a Computer Vision Scientist at Tandent, where he helped develop LightBrush™, the first commercial intrinsic imaging application. His work focuses on lighting-aware image understanding and synthesis by leveraging large amounts of data.
