The availability of academic and commercial light field camera systems has spurred significant research into the use of light fields and multi-view imagery in computer vision and computer graphics. In this talk, we discuss our results over the past few years, focusing on a few themes. First, we describe our work on a unified formulation of shape from light field cameras, combining cues such as defocus, correspondence, and shading. Then, we go beyond photoconsistency, addressing non-Lambertian objects, occlusions, and an SVBRDF-invariant shape recovery algorithm. Finally, we show that advances in machine learning can be used to interpolate light fields from very sparse angular samples, in the limit a single 2D image, and create light field videos from sparse temporal samples. We also discuss recent work on combining machine learning with plenoptic sampling theory to create virtual explorations of real scenes from a very sparse set of input images captured on a handheld mobile phone.
Ravi Ramamoorthi is the Ronald L. Graham professor of Computer Science at the University of California, San Diego, and founding Director of the UC San Diego Center for Visual Computing. He received his Ph.D. at Stanford in 2002, and earlier held tenured faculty positions at Columbia University and UC Berkeley. Prof. Ramamoorthi is an author of more than 150 refereed publications in computer graphics and computer vision, including 75 at ACM SIGGRAPH/TOG, and has played a key role in building multi-faculty research groups that have been recognized as leaders in computer graphics and computer vision at Columbia, Berkeley and UCSD. His research has been recognized with a half-dozen early career awards, including the ACM SIGGRAPH Significant New Researcher Award in computer graphics in 2007, and the Presidential Early Career Award for Scientists and Engineers (PECASE) for his work in physics-based computer vision in 2008. He was elevated to IEEE and ACM Fellow in 2017, and inducted into the SIGGRAPH Academy in 2019.