  An autostereogram, a.k.a. magic eye image, is a single-image stereogram that
can create visual illusions of 3D scenes from 2D textures. This paper studies
an interesting question that whether a deep CNN can be trained to recover the
depth behind an autostereogram and understand its content. The key to the
autostereogram magic lies in the stereopsis - to solve such a problem, a model
has to learn to discover and estimate disparity from the quasi-periodic
textures. We show that deep CNNs embedded with disparity convolution, a novel
convolutional layer proposed in this paper that simulates stereopsis and
encodes disparity, can nicely solve such a problem after being sufficiently
trained on a large 3D object dataset in a self-supervised fashion. We refer to
our method as ``NeuralMagicEye''. Experiments show that our method can
accurately recover the depth behind autostereograms with rich details and
gradient smoothness. Experiments also show the completely different working
mechanisms for autostereogram perception between neural networks and human
eyes. We hope this research can help people with visual impairments and those
who have trouble viewing autostereograms. Our code is available at
\url{https://jiupinjia.github.io/neuralmagiceye/}.
