Interpreting the Structure of Single Images by Learning from Examples

Abstract

An important problem in computer vision is the interpretation of the content of a single image. In our work we investigated the challenging case of recovering the underlying 3D structure of a scene from a single image, by learning from trainig data. Toward this, we developed a plane detection algorithm, which is able to find planar surfaces in a single still image and estimate their orientation with respect to the camera. This comprises two parts: a plane recognition stage, to classify individual regions as being planar or not, and to estimate their orienation; followed by a Markov-random field based segmentation stage to find distinct planes in the image. We also demonstrated an application of this to visual odometry, where single-image plane detection allows structure-rich maps to be built quickly. 

(Please note that this abstract does not appear in the submitted article itself, since that is itself an extended thesis abstract! But the above describes the main points of our work as described in our submission.)