  We present an algorithm to estimate fast and accurate depth maps from light
fields via a sparse set of depth edges and gradients. Our proposed approach is
based around the idea that true depth edges are more sensitive than texture
edges to local constraints, and so they can be reliably disambiguated through a
bidirectional diffusion process. First, we use epipolar-plane images to
estimate sub-pixel disparity at a sparse set of pixels. To find sparse points
efficiently, we propose an entropy-based refinement approach to a line estimate
from a limited set of oriented filter banks. Next, to estimate the diffusion
direction away from sparse points, we optimize constraints at these points via
our bidirectional diffusion method. This resolves the ambiguity of which
surface the edge belongs to and reliably separates depth from texture edges,
allowing us to diffuse the sparse set in a depth-edge and occlusion-aware
manner to obtain accurate dense depth maps.
