Papers › Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, Joshua B. Tenenbaum
We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets. The benefits of our model are three-fold: first, the use of an adversarial criterion, instead of traditional heuristic criteria, enables the generator to capture object structure implicitly and to synthesize high-quality 3D objects; second, the generator establishes a mapping from a low-dimensional probabilistic space to the space of 3D objects, so that we can sample objects without a reference image or CAD models, and explore the 3D object manifold; third, the adversarial discriminator provides a powerful 3D shape descriptor which, learned without supervision, has wide applications in 3D object recognition. Experiments demonstrate that our method generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods.
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Point Cloud Linear Classification | ModelNet40 | 3D-GAN | Overall Accuracy | 83.3 | #20 of 20 | Archive leaderboard | report |
| 3D Shape Classification | Pix3D | 3D-VAE-GAN | R@1 | 0.02 | #3 of 3 | Archive leaderboard | report |
| 3D Shape Classification | Pix3D | 3D-VAE-GAN | R@16 | 0.21 | #3 of 3 | Archive leaderboard | report |
| 3D Shape Classification | Pix3D | 3D-VAE-GAN | R@2 | 0.03 | #3 of 3 | Archive leaderboard | report |
| 3D Shape Classification | Pix3D | 3D-VAE-GAN | R@32 | 0.34 | #3 of 3 | Archive leaderboard | report |
| 3D Shape Classification | Pix3D | 3D-VAE-GAN | R@4 | 0.07 | #3 of 3 | Archive leaderboard | report |
| 3D Shape Classification | Pix3D | 3D-VAE-GAN | R@8 | 0.12 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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