In the panorama stitching process, no user input is needed.

Finally, a spherical panorama is obtained by warping the fisheye images. Then the correspondences are used to refine the estimation of rotation and intrinsic parameters. In the panorama stitching, we use scale invariant feature transform (SIFT) features and RANSAC to get the fisheye correspondences. In the calibration, the field of view and circular boundary are used to initialize the focal length, and line perspective constraint on sphere is used to refine the intrinsic parameters. The method contains fisheye camera calibration and panorama stitching. In this paper, we propose an automatic spherical panorama generation method with two fisheye images. Former panorama generation methods with fisheye images either require pre-calibrating the rotation of fisheye cameras with a calibration rig or manually selecting correspondences between images. Panoramas are often used to enhance immersive experiences. Our system accelerates graphics computation by a factor of 6 on a Google Cardboard display. We further parallelize the algorithm using GPU to achieve a responsive interface and validate our results via a user study. the acuity map and the saliency map), can greatly reduce the number of pixels to be processed. Our foveated stitching algorithm takes Videos from multiple cameras as input, combined with measurements of human visual attention (i.e. In this paper, we propose a real-time 360° Video foveated stitching framework, that renders the entire scene in different level of detail, aiming to create a high-resolution panoramic Video in real-time that can be streamed directly to the client. Although many capture devices are being released, getting high-resolution panoramas and displaying a virtual world in real-time remains challenging due to its computationally demanding nature. In virtual reality (VR) applications, the contents are usually generated by creating a 360° Video panorama of a real-world scene.

Panorama video for stitch