DAISY: A Fast Descriptor for Dense Wide Baseline Stereo and Multiview Reconstruction

Stereo reconstruction is a fundamental problem of computer vision. It has been studied for more than three decades and significant progress has been made in recent years as evidenced by the quality of the models now being produced. This is highly related with the advances in other fields. With the emergence of low cost high-quality cameras, we now live in an era where there is an abundant amount of data for use in reconstruction. The multi-tude of images with numerous sources of capture arose new interest in the stereo vision community due to new challenges such as being robust to photometric and geometric variability, scalability issues related to number of images and image resolutions. In this thesis, we aim to find efficient, and therefore practical, algorithmic solutions for the two extreme ends of stereo vision problem: first, we con-sider only two input image case where the cameras are placed far from each other and then we investigate the large scale multi-view reconstruc