Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks
摘要 卷积神经网络的最新进展已显示出有希望的3D形状完成结果。 由于GPU内存的限制，这些方法只能产生低分辨率的输出。 为了用语义上的合理性和上下文详细信息修补3D模型，我们引入了一个混合框架，该框架结合了3DEncoder-Decoder生成对抗网络（3D-ED-GAN）和长期递归卷积网络（LRCN）。 3DED-GANisa3D卷积神经网络经过生成对抗模型进行训练，以低分辨率填充丢失的3D数
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相关文章
- 1. Generative Adversarial Network (GAN) papers (2018.2.4 更新）
- 2. (zhuan) Recurrent Neural Network
- 3. Image Inpainting via Generative Multi-column Convolutional Neural Networks
- 4. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
- 5. Enhancing Underwater Imagery using Generative Adversarial Networks
- 6. [Style Transfer]——Blood Vessel Geometry Synthesis using Generative Adversarial Networks
- 7. Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
- 8. Generative Adversarial Nets[CycleGAN]
- 9. Generative Adversarial Text to Image Synthesis
- 10. Graphical Generative Adversarial Networks