Learning Representations and Generative Models for 3D Point Clouds
This work addresses the challenge of handling 3D geometric data for researchers and practitioners in computer vision and graphics, offering incremental improvements in representation learning and generative modeling.
The paper tackles the problem of learning representations and generative models for 3D point clouds, achieving state-of-the-art reconstruction quality and outperforming existing methods on 3D recognition tasks, with Gaussian Mixture Models in the latent space yielding the best results in fidelity and diversity.
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 (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation, as well as shape completion. We perform a thorough study of different generative models including GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs, and Gaussian Mixture Models (GMMs). To quantitatively evaluate generative models we introduce measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs yield the best results overall.