Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Point Cloud Linear Classification ModelNet40 3D-GAN Overall Accuracy 83.3 # 16

Results from Other Papers

Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
3D Shape Classification Pix3D 3D-VAE-GAN R@1 0.02 # 3
R@16 0.21 # 3
R@2 0.03 # 3
R@32 0.34 # 3
R@4 0.07 # 3
R@8 0.12 # 3


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