Deep Stereopsis for Magic Eye Images

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Added to collectionTop 100 Deep Learning and AI Papers 2021

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Source arXiv

Computer Vision

Deep Stereopsis for Magic Eye Images

NeuralMagicEye: Learning to See and Understand the Scene Behind an Autostereogram

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.

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.

Experiments show that our method can accurately recover the depth behind autostereograms with rich details and gradient smoothness.

Experiments also show the completely different workingmechanisms for autostereogram perception between neural networks and human eyes.

Authors

Zhengxia Zou, Tianyang Shi, Yi Yuan, Zhenwei Shi

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