Multiview monocular depth estimation using unsupervised learning methods

Abstract

Existing learned methods for monocular depth estimation use only a single view of scene for depth evaluation, so they inherently overt to their training scenes and cannot generalize well to new datasets. This thesis presents a neural network for multiview monocular depth estimation. Teaching a network to estimate depth via structure from motion allows it to generalize better to new environments with unfamiliar objects. This thesis extends recent work in unsupervised methods for single-view monocular depth estimation and uses the reconstruction losses for training posed in those works. Models and baseline models were evaluated on a variety of datasets and results indicate that indicate multiview models generalize across datasets better than previous work. This work is unique in that it emphasizes cross domain performance and ability to generalize more so than performance on the training set.