Evaluating the role of context in 3D theater stage reconstruction

Abstract

Recovering the 3D structure from 2D images is a problem dating back to the 1960s. It is only recently, with the advancement of computing technology, that there has been substantial progress in solving this problem. In this thesis, we focus on one method for recovering scene structure given a single image. This method uses supervised learning techniques and a multiple-segmentation framework for adding contextual information to the inference. We evaluate the effect of this added contextual information by excluding this additional information to measure system performance. We then go on to evaluate the effect of the other system components that remain which include classifiers and image features. For example, in the case of classifiers, we substitute the original with others to see the level of accuracy that these provide. In the case of the features, we conduct experiments that give us the most important features that contribute to classification accuracy. All of this put together lets us evaluate the effect of adding contextual information to the learning process and if it can be improved by improving the other non-contextual components of the system.