Depth from Familiar Objects: A Hierarchical Model for 3D Scenes

Abstract

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. Applied to a dataset of office scenes, our model detects objects at multiple scales via a coarse reconstruction of the corresponding 3D geometry.