  What is a good vector representation of an object? We believe that it should
be generative in 3D, in the sense that it can produce new 3D objects; as well
as be predictable from 2D, in the sense that it can be perceived from 2D
images. We propose a novel architecture, called the TL-embedding network, to
learn an embedding space with these properties. The network consists of two
components: (a) an autoencoder that ensures the representation is generative;
and (b) a convolutional network that ensures the representation is predictable.
This enables tackling a number of tasks including voxel prediction from 2D
images and 3D model retrieval. Extensive experimental analysis demonstrates the
usefulness and versatility of this embedding.
