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arXiv · 1705.05994

Learning a Hierarchical Latent-Variable Model of 3D Shapes

Shikun Liu·C. Lee Giles·Alexander G. Ororbia II
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Abstract

We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion. Through the use of skip-connections, our model can successfully learn and infer a latent, hierarchical representation of objects. Furthermore, realistic 3D objects can be easily generated by sampling the VSL's latent probabilistic manifold. We show that our generative model can be trained end-to-end from 2D images to perform single image 3D model retrieval. Experiments show, both quantitatively and qualitatively, the improved generalization of our proposed model over a range of tasks, performing better or comparable to various state-of-the-art alternatives.

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Shikun Liu, C. Lee Giles, Alexander G. Ororbia II. 2017-05-17. Learning a Hierarchical Latent-Variable Model of 3D Shapes. https://arxiv.org/abs/1705.05994

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