Effectively and accurately synthesizing point clouds is yet a challenge task due to the high-dimensionality of the observation space. In this article, we focus on the generative model to capture the informative intrinsic structure in the latent low-dimensional space and the diversity in the ambient space. We propose a new dual adversarial model which combines Generative Adversarial Network (GAN) and AutoEncoder (AE) with well-designed loss. Extensive experiments demonstrate the effectiveness of the framework on 3D point clouds synthesis. The dual adversarial model achieves the state-of-the-art performance comparing with several well-known point clouds generation models. We also perform experiments on MNIST dataset that exhibit the competitive performance on 2D data.
Summary:
The paper focuses on designing a generative framework for 3-D point data clouds. These point clouds correspond to objects shapes in 3-dimensions. According to the paper, previous approaches for generating such 3-D point clouds involved autoencoder and GANs used separately. The authors propose a framework combining both autoencoder and GAN in a single network. The authors claim that part of the network learns effective latent space embedding for 3-d point clouds corresponding to different objects and thus the entire network is more effective in generating 3-d point cloud. Experimental results are presented to support the claims for efficient embeddings, and better generation of 3-d point clouds.
Decision
The paper has some lacking in explanation. I am particularly interested in the answers to the following questions:
1.	The encoder module (denoted as E in the paper) uses a loss function that apparently does not involve the encoder output (z in the paper). How the module weights can be updated this way is unclear.
2.	G1 is updated twice in each iteration, according to algorithm 1 in paper (once during update of 𝜽G1 and 𝜽G). How the generator G1’s output manages to converge to output z from encoder E is unclear.
3.	The authors claim that the proposed model has both better performance and efficiency. Although experimental results are provided to support claim for better performance, none have been offered for efficiency claim. (calculation of EMD distance seems to be very expensive computationally)
Feedback
For section 5.5, a quantitative analysis might make the claim for portability of the framework stronger. In paragraph 1 of introduction, disadvantage of 3d point cloud data can be better explained.