A common feature-based disparity control strategy in stereoscopic panorama generation

In this paper, we present a solution for the generation of high-resolution stereoscopic panoramas. Many existing monocular approaches can produce high-quality panoramas; however, direct application of these techniques to the stereo case often fail to deliver high-quality stereo vision. Compared to generating a high-quality monocular panorama, generating a high-quality stereo panorama using existing techniques is challenging owing to the inconsistency between the left and right views and difficulties in disparity control. In our proposed disparity control strategy, one commonly identified feature set is utilized to describe the identical feature in two pairs of adjacent input images instead of processing the left and right images independently. In addition, the conventional RANSAC algorithm is extended to the stereo case to further improve the stereo consistency. After the aligning and blending steps, a global panorama disparity correction technique is applied to compensate for the vertical disparity and correct horizontal disparity. The experiments show that our method can enforce left and right input images to be stitched by very similar pose parameters and generate high-quality stereo panoramas that provide a pleasant 3D viewing experience.