Photo: Wolfram Scheible.
A fundamental problem in computer vision is the reconstruction of the 3D world. This has applications as varied self-driving cars, 3D mapping, virtual reality, graphics, and robotics. We think that reasoning about the 3D world and its structure is at the heart of computer vision. In Perceiving Systems we study the recovery and reconstruction of 3D structure from single images, RGB-D data, video sequences, stereo, and multi-view stereo.
Our major innovations lie in combining high-level and semantic cues with low-level features. We view the problem as the integration of model fitting with dense structure recovery. While much of our work has focused on object-specific models like people and cars, we are particularly interested in generic representations and compositional models of objects and scenes.
Increases in computing power, labeled training data, large databases of 3D CAD models, 3D sensors, and open-source rendering engines, are all opening new opportunities to model and infer 3D objects and scenes.