  We present Extreme View Synthesis, a solution for novel view extrapolation
that works even when the number of input images is small--as few as two. In
this context, occlusions and depth uncertainty are two of the most pressing
issues, and worsen as the degree of extrapolation increases. We follow the
traditional paradigm of performing depth-based warping and refinement, with a
few key improvements. First, we estimate a depth probability volume, rather
than just a single depth value for each pixel of the novel view. This allows us
to leverage depth uncertainty in challenging regions, such as depth
discontinuities. After using it to get an initial estimate of the novel view,
we explicitly combine learned image priors and the depth uncertainty to
synthesize a refined image with less artifacts. Our method is the first to show
visually pleasing results for baseline magnifications of up to 30X.
