  Recovering the 3D structure of the scene from images yields useful
information for tasks such as shape and scene recognition, object detection, or
motion planning and object grasping in robotics. In this thesis, we introduce a
general machine learning approach called unsupervised CRF learning based on
maximizing the conditional likelihood. We apply our approach to computer vision
systems that recover the 3-D scene geometry from images. We focus on recovering
3D geometry from single images, stereo pairs and video sequences. Building
these systems requires algorithms for doing inference as well as learning the
parameters of conditional Markov random fields (MRF). Our system is trained
unsupervisedly without using ground-truth labeled data. We employ a
slanted-plane stereo vision model in which we use a fixed over-segmentation to
segment the left image into coherent regions called superpixels, then assign a
disparity plane for each superpixel. Plane parameters are estimated by solving
an MRF labelling problem, through minimizing an energy fuction. We demonstrate
the use of our unsupervised CRF learning algorithm for a parameterized
slanted-plane stereo vision model involving shape from texture cues. Our stereo
model with texture cues, only by unsupervised training, outperforms the results
in related work on the same stereo dataset. In this thesis, we also formulate
structure and motion estimation as an energy minimization problem, in which the
model is an extension of our slanted-plane stereo vision model that also
handles surface velocity. Velocity estimation is achieved by solving an MRF
labeling problem using Loopy BP. Performance analysis is done using our novel
evaluation metrics based on the notion of view prediction error. Experiments on
road-driving stereo sequences show encouraging results.
