Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder network for point clouds, which outperforms the state of the art in 3D recognition tasks. We also design GAN architectures to generate novel point-clouds. Importantly, we show that by training the GAN in the latent space learned by the autoencoder, we greatly boost the GAN's data-generating capacity, creating significantly more diverse and realistic geometries, with far simpler architectures. The expressive power of our learned embedding, obtained without human supervision, enables basic shape editing applications via simple algebraic manipulations, such as semantic part editing and shape interpolation.
Achlioptas et al. proposed LatentGAN by introducing a new deep auto-encoder network with state-of-the-art reconstruction quality and generalization ability for point cloud data REF .
Representation Learning and Adversarial Generation of 3D Point Clouds
35th International Conference on Machine Learning (ICML), 2018
Computer Science