Learning a hierarchical latent-variable model of 3D shapes

Shikun Liu, Lee Giles, Alexander Ororbia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 International Conference on 3D Vision, 3DV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages542-551
Number of pages10
ISBN (Electronic)9781538684252
DOIs
StatePublished - Oct 12 2018
Event6th International Conference on 3D Vision, 3DV 2018 - Verona, Italy
Duration: Sep 5 2018Sep 8 2018

Publication series

NameProceedings - 2018 International Conference on 3D Vision, 3DV 2018

Other

Other6th International Conference on 3D Vision, 3DV 2018
CountryItaly
CityVerona
Period9/5/189/8/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Liu, S., Giles, L., & Ororbia, A. (2018). Learning a hierarchical latent-variable model of 3D shapes. In Proceedings - 2018 International Conference on 3D Vision, 3DV 2018 (pp. 542-551). [8491006] (Proceedings - 2018 International Conference on 3D Vision, 3DV 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/3DV.2018.00068