- Title: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
- Authors: Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Figure 1: The generator of 3D Generative Adversarial Network (3D-GAN)
Figure 2: Shapes synthesized by 3D-GAN
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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- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling, Jiajun Wu*, Chengkai Zhang*, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum
- MarrNet: 3D Shape Reconstruction via 2.5D Sketches Jiajun Wu*, Yifan Wang*, Tianfan Xue, Xingyuan Sun, William T. Freeman, and Joshua B. Tenenbaum