  The 3D-zoom operation is the positive translation of the camera in the
Z-axis, perpendicular to the image plane. In contrast, the optical zoom changes
the focal length and the digital zoom is used to enlarge a certain region of an
image to the original image size. In this paper, we are the first to formulate
an unsupervised 3D-zoom learning problem where images with an arbitrary zoom
factor can be generated from a given single image. An unsupervised framework is
convenient, as it is a challenging task to obtain a 3D-zoom dataset of natural
scenes due to the need for special equipment to ensure camera movement is
restricted to the Z-axis. In addition, the objects in the scenes should not
move when being captured, which hinders the construction of a large dataset of
outdoor scenes. We present a novel unsupervised framework to learn how to
generate arbitrarily 3D-zoomed versions of a single image, not requiring a
3D-zoom ground truth, called the Deep 3D-Zoom Net. The Deep 3D-Zoom Net
incorporates the following features: (i) transfer learning from a pre-trained
disparity estimation network via a back re-projection reconstruction loss; (ii)
a fully convolutional network architecture that models depth-image-based
rendering (DIBR), taking into account high-frequency details without the need
for estimating the intermediate disparity; and (iii) incorporating a
discriminator network that acts as a no-reference penalty for unnaturally
rendered areas. Even though there is no baseline to fairly compare our results,
our method outperforms previous novel view synthesis research in terms of
realistic appearance on large camera baselines. We performed extensive
experiments to verify the effectiveness of our method on the KITTI and
Cityscapes datasets.
