  There is some ambiguity in the 3D shape of an object when the number of
observed views is small. Because of this ambiguity, although a 3D object
reconstructor can be trained using a single view or a few views per object,
reconstructed shapes only fit the observed views and appear incorrect from the
unobserved viewpoints. To reconstruct shapes that look reasonable from any
viewpoint, we propose to train a discriminator that learns prior knowledge
regarding possible views. The discriminator is trained to distinguish the
reconstructed views of the observed viewpoints from those of the unobserved
viewpoints. The reconstructor is trained to correct unobserved views by fooling
the discriminator. Our method outperforms current state-of-the-art methods on
both synthetic and natural image datasets; this validates the effectiveness of
our method.
