Escaping Plato's Cave: 3D Shape From Adversarial Rendering

ICCV 2019 Philipp HenzlerNiloy MitraTobias Ritschel

We introduce PlatonicGAN to discover the 3D structure of an object class from an unstructured collection of 2D images, i.e., where no relation between photos is known, except that they are showing instances of the same category. The key idea is to train a deep neural network to generate 3D shapes which, when rendered to images, are indistinguishable from ground truth images (for a discriminator) under various camera poses... (read more)

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