Learning a Hierarchical Latent-Variable Model of 3D Shapes

17 May 2017  ·  Shikun Liu, C. Lee Giles, Alexander G. Ororbia II ·

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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Object Recognition ModelNet40 Variational Shape Learner Accuracy 84.5% # 4

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