Papers › Learning a Hierarchical Latent-Variable Model of 3D Shapes
Learning a Hierarchical Latent-Variable Model of 3D Shapes
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Recognition | ModelNet40 | Variational Shape Learner | Accuracy | 84.5% | #6 of 6 | Archive leaderboard | report |
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