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2018 International Conference on 3D Vision (3DV) (2018)
Verona, Italy
Sep 5, 2018 to Sep 8, 2018
ISSN: 2475-7888
ISBN: 978-1-5386-8425-2
pp: 542-551
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.
INDEX TERMS
image reconstruction, image representation, image retrieval, learning (artificial intelligence), probability, solid modelling
CITATION

S. Liu, L. Giles and A. Ororbia, "Learning a Hierarchical Latent-Variable Model of 3D Shapes," 2018 International Conference on 3D Vision (3DV), Verona, Italy, 2018, pp. 542-551.
doi:10.1109/3DV.2018.00068
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