  360 images represent scenes captured in all possible viewing directions and
enable viewers to navigate freely around the scene thereby providing an
immersive experience. Conversely, conventional images represent scenes in a
single viewing direction with a small or limited field of view (FOV). As a
result, only certain parts of the scenes are observed, and valuable information
about the surroundings is lost. In this paper, a learning-based approach that
reconstructs the scene in 360 x 180 from a sparse set of conventional images
(typically 4 images) is proposed. The proposed approach first estimates the FOV
of input images relative to the panorama. The estimated FOV is then used as the
prior for synthesizing a high-resolution 360 panoramic output. The proposed
method overcomes the difficulty of learning-based approach in synthesizing high
resolution images (up to 512$\times$1024). Experimental results demonstrate
that the proposed method produces 360 panorama with reasonable quality. Results
also show that the proposed method outperforms the alternative method and can
be generalized for non-panoramic scenes and images captured by a smartphone
camera.
