  We propose an unsupervised real-time dense depth completion from a sparse
depth map guided by a single image. Our method generates a smooth depth map
while preserving discontinuity between different objects. Our key idea is a
Binary Anisotropic Diffusion Tensor (B-ADT) which can completely eliminate
smoothness constraint at intended positions and directions by applying it to
variational regularization. We also propose an Image-guided Nearest Neighbor
Search (IGNNS) to derive a piecewise constant depth map which is used for B-ADT
derivation and in the data term of the variational energy. Our experiments show
that our method can outperform previous unsupervised and semi-supervised depth
completion methods in terms of accuracy. Moreover, since our resulting depth
map preserves the discontinuity between objects, the result can be converted to
a visually plausible point cloud. This is remarkable since previous methods
generate unnatural surface-like artifacts between discontinuous objects.
