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arXiv · 1607.00662

Unsupervised Learning of 3D Structure from Images

Danilo Jimenez Rezende·S. M. Ali Eslami·Shakir Mohamed·Peter Battaglia·Max Jaderberg·Nicolas Heess
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Abstract

A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several datasets, including ShapeNet [2], and establish the first benchmarks in the literature. We also show how these models and their inference networks can be trained end-to-end from 2D images. This demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner.

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Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess. 2016-07-03. Unsupervised Learning of 3D Structure from Images. https://arxiv.org/abs/1607.00662

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