ABSTRACT
This chapter concentrates on dense image correspondence estimation with a special focus on stereo. Images are the basic input for a vast majority of algorithms dealing with the reconstruction of the real world. To analyze a scene from a collection of images it becomes inevitable to put these images into correspondence. These correspondences then form the basis for many subsequent analyses, including camera calibration, stereo and 3D reconstruction, motion information, scene flow, and others. While some of these tasks like camera calibration require only sparse correspondences between the images (Chapter 7), others require per-pixel correspondence, also known as dense correspondence estimation.
