Point clouds have recently become an important visual representation format, especially for virtual and augmented reality applications, thus making point cloud coding a very hot research topic. Deep learning-based coding methods have recently emerged in the field of image coding with increasing success. These coding solutions take advantage of the ability of convolutional neural networks to extract adaptive features from the images to create a latent representation that can be efficiently coded. In this context, this paper extends the deep-learning coding approach to point cloud coding using an autoencoder network design. Performance results are very promising, showing improvements over the Point Cloud Library codec often taken as benchmark, thus suggesting a significant margin of evolution for this new point cloud coding paradigm.