This talk will show my work on understanding the physical world from images, done in conjunction with my students both past and present. I will first show how we can reconstruct 3D scenes, including invisible surfaces, from a single RGB image. We have developed an approach that learns to predict a scene-scale implicit function using realistic 3D supervision that can be gathered by consumers or robots instead of by using artist-created watertight 3D assets. After showing reconstructions from our system in everyday scenarios, I will talk about how measuring the world can unlock new insights in science, from millimeter-sized bird bones to solar physics data where a pixel is a few hundred miles wide. I will conclude by showing work towards understanding interaction, especially focusing on hands and the objects they hold.

Bio:

David Fouhey is an assistant professor at the University of Michigan. He received a Ph.D. in robotics from Carnegie Mellon University and was then a postdoctoral fellow at UC Berkeley. His work has been recognized by a NSF CAREER award, and NSF and NDSEG fellowships. He has spent time at the University of Oxford’s Visual Geometry Group, INRIA Paris, and Microsoft Research.