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

NeurIPS 2016 Jiajun WuChengkai ZhangTianfan XueWilliam T. FreemanJoshua 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... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
3D Shape Classification Pix3D 3D-VAE-GAN [email protected] 0.02 # 3
[email protected] 0.21 # 3
[email protected] 0.03 # 3
[email protected] 0.34 # 3
[email protected] 0.07 # 3
[email protected] 0.12 # 3

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