  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 wider 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.
