  This paper presents a learning-based approach to synthesize the view from an
arbitrary camera position given a sparse set of images. A key challenge for
this novel view synthesis arises from the reconstruction process, when the
views from different input images may not be consistent due to obstruction in
the light path. We overcome this by jointly modeling the epipolar property and
occlusion in designing a convolutional neural network. We start by defining and
computing the aperture disparity map, which approximates the parallax and
measures the pixel-wise shift between two views. While this relates to
free-space rendering and can fail near the object boundaries, we further
develop a warping confidence map to address pixel occlusion in these
challenging regions. The proposed method is evaluated on diverse real-world and
synthetic light field scenes, and it shows better performance over several
state-of-the-art techniques.
