text: This paper introduces a generative approach for 3D point clouds. More specifically, two Generative Adversarial approaches are introduced: Raw point cloud GAN, and Latent-space GAN (r-GAN and l-GAN as referred to in the paper). In addition, a GMM sampling + GAN decoder approach to generation is also among the experimented variations. The results look convincing for the generation experiments in the paper, both from class-specific (Figure 1) and multi-class generators (Figure 6). The quantitative results also support the visuals. One question that arises is whether the point cloud approaches to generation is any more valuable compared to voxel-grid based approaches. Especially Octree based approaches [1-below] show very convincing and high-resolution shape generation results, whereas the details seem to be washed out for the point cloud results presented in this paper. I would like to see comparison experiments with voxel based approaches in the next update for the paper. [1] @article{tatarchenko2017octree, title={Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs}, author={Tatarchenko, Maxim and Dosovitskiy, Alexey and Brox, Thomas}, journal={arXiv preprint arXiv:1703.09438}, year={2017} }