The importance of making computers understand the scene presented to them cannot be understated. The ability to automatically infer the semantics and geometry of any given scene would enable a variety of different applications in the field of Augmented reality, Robotics, Image Processing and Visualization. Understandably, a large amount of research effort has been directed at this problem in the computer vision and machine learning communities, with plenty of motivation and interest in computer graphics. The availability of commodity depth sensors have led to a number of breakthroughs to made in this space. Much of this success can be attributed to the use of computer graphics for generating realistic sensor data. We believe the time is ripe for extending this promising approach to the more challenging problem of full scene understanding. However, to enable this, we need close collaboration between researchers from machine learning, computer vision, and computer graphics. This workshop is intended to bring researchers from these communities together.

Workshop Website

Presenter(s)

Annotating RGBD Images of Indoor Scenes
Yu-Shiang Wong, Hung-Kuo Chu

Blind Recovery of Spatially Varying Reflectance from a Single Image
Kevin Karsch, David Forsyth

MCGraph: Multi-criterion Representation for Scene Understanding
Moos Hueting, Aron Monszpart, Nicolas Mellado

On Being the Right Scale: Sizing Large Collections of 3D Models
Manolis Savva, Angel X. Chang, Gilbert Bernstein, Christopher D. Manning, Pat Hanrahan