Learning a Hierarchical Latent-Variable Model of 3D Shapes
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We propose the Variational Shape Learner (VSL), a hierarchical latent-variable model for 3D shape learning. VSL employs an unsupervised approach to learning and inferring the underlying structure of voxelized 3D shapes. Through the use of skip-connections, our model can successfully learn 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 performance of our proposed model over a range of tasks.
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