1. Introduction
Panorama stitching is a well studied topic and many software tools are available for users to create panoramas [26]. Most of these methods, however, are designed for monocular image stitching. Employing a monocular image stitching method to independently create the left and right view of a stereoscopic panorama is problematic as the left and right panorama may not be consistent. As shown in Figure 1 (a), the cat in the left panorama is different from that in the right panorama. This is because the input images are taken at different time and the cat appears different in the input images. The left and right panorama take the cat from different input images. The inconsistency will lead to “retinal rivalry” and bring in “3D fatigue” to viewers [17]. Moreover, stereoscopic images have an extra dimension of disparity, which cannot be taken care of by independently stitching the two views. Figure 1 (a) shows that the resulting panorama has vertical disparities in the car headlight and tire area. This will also compromise the 3D viewing experience. Dedicated stereoscopic image stitching methods have been developed [8], [19], [21]. However, these methods require a user to densely sample the scene using a video camera and/or follow some specific rules to rotate the camera and cannot work well with a sparse set of casually taken input images.