Computer Vision: Learning Shading and Lighting without Ground Truth

Presenter: David Forsyth, Department of Computer Science, University of Illinois

Tuesday, April 26, 2022




Computer vision research has been revolutionized by a relatively straightforward recipe: obtain data, and apply modern classification or regression techniques, as appropriate. This recipe has solved commercially valuable problems and built fame and fortune for many. But it is expensive and hard to use without large engineering teams and a lot of money. Finally, there are many complicated details that need working out.

I contend that academic computer vision research, rather than working out these details, should look beyond this recipe. What do we do if we don’t have, or can’t get, appropriately labelled data? Two natural strategies — fake the data, or find mathematical structure seem particularly promising.

I will use intrinsic image decomposition take a picture, and decompose into albedo and shading to illustrate the value of faking data. This is a classical problem for which simple faked data produces notably better results than either CGI data (hard and unreliable fakery) or real data (hard to get).

I will use scene relighting take a picture of a scene, and make it look as though the light were different to illustrate the value of mathematical structure. This is a novel problem that can draw on a long mathematical tradition.


I am currently Fulton-Watson-Copp chair in computer science at U. Illinois at Urbana-Champaign, where I moved from U.C Berkeley, where I was also full professor. I have occupied the Fulton-Watson-Copp chair in Computer Science at the University of Illinois since 2014. I have published over 170 papers on computer vision, computer graphics and machine learning. I have served as program co-chair for IEEE Computer Vision and Pattern Recognition in 2000, 2011, 2018 and 2021, general co-chair for CVPR 2006 and 2015 and ICCV 2019, program co-chair for the European Conference on Computer Vision 2008, and am a regular member of the program committee of all major international conferences on computer vision. I have served six years on the SIGGRAPH program committee, and am a regular reviewer for that conference. I have received best paper awards at the International Conference on Computer Vision and at the European Conference on Computer Vision. I received an IEEE technical achievement award for 2005 for my research. I became an IEEE Fellow in 2009, and an ACM Fellow in 2014. My textbook, “Computer Vision: A Modern Approach” (joint with J. Ponce and published by Prentice Hall) is now widely adopted as a course text (adoptions include MIT, U. Wisconsin-Madison, UIUC, Georgia Tech and U.C. Berkeley). A further textbook, Probability and Statistics for Computer Science, is in print; yet another (Applied Machine Learning) has just appeared. I have served two terms as Editor in Chief, IEEE TPAMI. I serve on a number of scientific advisory boards, and have an active practice as an expert witness.