Event Location: NSH 1507
Bio: Jean-Francois Lalonde received his B.E. in Computer Engineering from Laval University, Canada in 2004. He received his M.S. in Robotics from Carnegie Mellon University in 2006 under Martial Hebert, and he has been a Robotics Ph.D. student advised by Alexei A. Efros in that institution since. His research interests are in computer vision and computer graphics, focusing on image understanding and synthesis leveraging large amounts of data.

Abstract: Given a single outdoor image, we present a method for estimating the likely illumination conditions of the scene. In particular, we compute the probability distribution over the sun position and visibility. The method relies on a combination of weak cues that can be extracted from different portions of the image: the sky, the vertical surfaces, and the ground. While no single cue can reliably estimate illumination by itself, each one can reinforce the others to yield a more robust estimate. This is combined with a data-driven prior computed over a dataset of 6 million Internet photos. We present quantitative results on a webcam dataset with annotated sun positions, as well as qualitative results on consumer-grade photographs downloaded from Internet. Based on the estimated illumination, we show how to realistically insert synthetic 3-D objects into the scene.