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arXiv · 1603.08637

Learning a Predictable and Generative Vector Representation for Objects

Rohit Girdhar·David F. Fouhey·Mikel Rodriguez·Abhinav Gupta
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Abstract

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.

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Rohit Girdhar, David F. Fouhey, Mikel Rodriguez, Abhinav Gupta. 2016-03-29. Learning a Predictable and Generative Vector Representation for Objects. https://arxiv.org/abs/1603.08637

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