Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtain meaningful representations of 3D shapes that can be used for challenging tasks, including 3D points generation, reconstruction, compression, and clustering. Contrary to existing methods for 3D point cloud generation that train separate decoupled models for representation learning and generation, our approach is the first end-to-end solution that allows to simultaneously learn a latent space of representation and generate 3D shape out of it. Moreover, our model is capable of learning meaningful compact binary descriptors with adversarial training conducted on a latent space. To achieve this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D input and create 3D output. Thanks to our end-to-end training regime, the resulting method called 3D Adversarial Autoencoder (3dAAE) obtains either binary or continuous latent space that covers a much broader portion of training data distribution. Finally, our quantitative evaluation shows that 3dAAE provides state-of-the-art results for 3D points clustering and 3D object retrieval.

深度生成架构提供了一种不仅对图像建模而且对复杂的3维对象（例如点云）建模的方法。在这项工作中，我们提出了一种新颖的方法来获取3D形状的有意义的表示形式，可用于具有挑战性的任务，包括3D点生成，重构，压缩和聚类。与现有的3D点云生成方法相反，该方法训练单独的解耦模型进行表示学习和生成，我们的方法是第一个端到端解决方案，允许同时学习潜在的表示空间并从中生成3D形状。此外，我们的模型能够通过在潜在空间上进行对抗训练来学习有意义的紧凑型二进制描述符。为了实现这个目标，我们扩展了深度对抗自动编码器模型（AAE），以接受3D输入并创建3D输出。多亏了我们的端到端训练制度，称为3D对抗自动编码器（3dAAE）的最终方法获得了覆盖训练数据分布范围更广的二进制或连续潜在空间。最后，我们的定量评估表明3dAAE为3D点聚类和3D对象检索提供了最新的结果。