  High-resolution depth map can be inferred from a low-resolution one with the
guidance of an additional high-resolution texture map of the same scene.
Recently, deep neural networks with large receptive fields are shown to benefit
applications such as image completion. Our insight is that super resolution is
similar to image completion, where only parts of the depth values are precisely
known. In this paper, we present a joint convolutional neural pyramid model
with large receptive fields for joint depth map super-resolution. Our model
consists of three sub-networks, two convolutional neural pyramids concatenated
by a normal convolutional neural network. The convolutional neural pyramids
extract information from large receptive fields of the depth map and guidance
map, while the convolutional neural network effectively transfers useful
structures of the guidance image to the depth image. Experimental results show
that our model outperforms existing state-of-the-art algorithms not only on
data pairs of RGB/depth images, but also on other data pairs like
color/saliency and color-scribbles/colorized images.
