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.

本文提出了一种新颖的框架，即3D生成对抗网络（3D-GAN），通过利用体积卷积网络和生成对抗网络的最新进展，从概率空间生成3D对象，并具有生成高质量3D对象、无先验CAD模型或图像依赖性、生成器从低维概率空间到3D空间的映射以及训练出的无监督深度学习的3D描述符等优点，在3D对象生成及识别等方面取得了令人印象深刻的性能。

通过三维生成对抗建模学习物体形状概率潜空间