22. E. Sudderth, A. Torralba, W. Freeman, A. Willsky: Depth from Familiar Objects: A Hierarchical Model for 3D Scenes
We develop an integrated, probabilistic model for the appearance
and three-dimensional geometry of cluttered scenes. Object categories are modeled
via distributions over the 3D location and appearance of visual features. Uncertainty
in the number of object instances depicted in a particular image is then achieved
via a transformed Dirichlet process.
In contrast with image-based approaches to object recognition, we model scale
variations as the perspective projection of objects in different 3D poses.
To calibrate the underlying geometry, we incorporate binocular stereo images
into the training process. A robust likelihood model accounts for outliers
in matched stereo features, allowing effective learning of 3D object structure
from partial 2D segmentations.