Stanford AI talk: Toward a Geometrically Coherent Image Interpretation

Alexei (Alyosha) Efros
April 10, 2006, 3:15PM (NOT 4:15PM)
http://graphics.stanford.edu/ba-colloquium/

Abstract
Image interpretation, the ability to see and understand the three-dimensional world behind a two-dimensional image, goes to the very heart of the computer vision problem. The ultimate objective is, given an image, to automatically produce a coherent interpretation of the depicted scene. This requires not only recognizing specific objects (e.g. people, houses, cars, trees), but understanding the underlying structure of the 3D scene where these objects reside.

In this talk I will describe some of our recent efforts toward this lofty goal. I will present an approach for estimating the coarse geometric properties of a scene by learning appearance-based models of geometric classes. Geometric classes describe the 3D orientation of image regions with respect to the camera. This geometric information is then combined with camera viewpoint estimation and local object detection producing a prototype for a coherent image-interpretation framework.

Joint work with Derek Hoiem and Martial Hebert at CMU.