Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
Point clouds are an important type of geometric data and have widespread use in computer graphics and vision. However, learning representations for point clouds is particularly challenging due to their nature as being an unordered collection of points irregularly distributed in 3D space. Graph convolution, a generalization of the convolution operation for data defined over graphs, has been recently shown to be very successful at extracting localized features from point clouds in supervised or semi-supervised tasks such as classification or segmentation. This paper studies the unsupervised problem of a generative model exploiting graph convolution. We focus on the generator of a GAN and define methods for graph convolution when the graph is not known in advance as it is the very output of the generator. The proposed architecture learns to generate localized features that approximate graph embeddings of the output geometry. We also study the problem of defining an upsampling layer in the graph-convolutional generator, such that it learns to exploit a self-similarity prior on the data distribution to sample more effectively.
 This paper proposes graph-convolutional GANs for irregular 3D point clouds that learn domain (the graph structure) and features at the same time. In addition, a method for upsampling at the GAN generator is introduced. The paper is very well written, addresses a relevant problem (classification of 3D point clouds with arbitrary, a priori unknown graph structure) in an original way, and supports the presented ideas with convincing experiments. It aggregates the latest developments in the field, the Wasserstein GAN, edge-conditional convolutions into a concise framework and designs a novel GAN generator. I have only some minor concerns: 1) My only serious concern is the degree of novelty with respect to (Achlioptas et al., 2017). The discriminator is the same and although the generator is a fully connected network in that paper, it would be good to highlight conceptual improvements as well as quantitative advantages of the paper at hand more thoroughly. Similarly, expanding a bit more on the differences and improvements over (Grover et al., 2018) would improve the paper. 2) P3, second to last line of 2.1: reference needs to be fixed "…Grover et al. (Grover et al., 2018)" 3) It would be helpful to highlight the usefulness of artificially generating irregular 3D point clouds from an application perspective, too. While GANs have various applications if applied to images it is not obvious how artificially created irregular 3D point clouds can be useful. Although the theoretical insights presented in the paper are exciting, a more high-level motivation would further improve its quality. 4) A discussion of shortcomings of the presented method seems missing. While it is understandable that emphasis is put on novelty and its advantages, it would be interesting to see where the authors see room for improvement.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
Point clouds are an important type of geometric data and have widespread use in computer graphics and vision. However, learning representations for point clouds is particularly challenging due to their nature as being an unordered collection of points irregularly distributed in 3D space. Graph convolution, a generalization of the convolution operation for data defined over graphs, has been recently shown to be very successful at extracting localized features from point clouds in supervised or semi-supervised tasks such as classification or segmentation. This paper studies the unsupervised problem of a generative model exploiting graph convolution. We focus on the generator of a GAN and define methods for graph convolution when the graph is not known in advance as it is the very output of the generator. The proposed architecture learns to generate localized features that approximate graph embeddings of the output geometry. We also study the problem of defining an upsampling layer in the graph-convolutional generator, such that it learns to exploit a self-similarity prior on the data distribution to sample more effectively.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper proposes graph-convolutional GANs for irregular 3D point clouds that learn domain (the graph structure) and features at the same time. In addition, a method for upsampling at the GAN generator is introduced. The paper is very well written, addresses a relevant problem (classification of 3D point clouds with arbitrary, a priori unknown graph structure) in an original way, and supports the presented ideas with convincing experiments. It aggregates the latest developments in the field, the Wasserstein GAN, edge-conditional convolutions into a concise framework and designs a novel GAN generator. I have only some minor concerns: 1) My only serious concern is the degree of novelty with respect to (Achlioptas et al., 2017). The discriminator is the same and although the generator is a fully connected network in that paper, it would be good to highlight conceptual improvements as well as quantitative advantages of the paper at hand more thoroughly. Similarly, expanding a bit more on the differences and improvements over (Grover et al., 2018) would improve the paper. 2) P3, second to last line of 2.1: reference needs to be fixed "…Grover et al. (Grover et al., 2018)" 3) It would be helpful to highlight the usefulness of artificially generating irregular 3D point clouds from an application perspective, too. While GANs have various applications if applied to images it is not obvious how artificially created irregular 3D point clouds can be useful. Although the theoretical insights presented in the paper are exciting, a more high-level motivation would further improve its quality. 4) A discussion of shortcomings of the presented method seems missing. While it is understandable that emphasis is put on novelty and its advantages, it would be interesting to see where the authors see room for improvement.
assistant
 This paper proposes graph-convolutional GANs for irregular 3D point clouds that learn domain (the graph structure) and features at the same time. In addition, a method for upsampling at the GAN generator is introduced. The paper is very well written, addresses a relevant problem (classification of 3D point clouds with arbitrary, a priori unknown graph structure) in an original way, and supports the presented ideas with convincing experiments. It aggregates the latest developments in the field, the Wasserstein GAN, edge-conditional convolutions into a concise framework and designs a novel GAN generator. I have only some minor concerns: 1) My only serious concern is the degree of novelty with respect to (Achlioptas et al., 2017). The discriminator is the same and although the generator is a fully connected network in that paper, it would be good to highlight conceptual improvements as well as quantitative advantages of the paper at hand more thoroughly. Similarly, expanding a bit more on the differences and improvements over (Grover et al., 2018) would improve the paper. 2) P3, second to last line of 2.1: reference needs to be fixed "…Grover et al. (Grover et al., 2018)" 3) It would be helpful to highlight the usefulness of artificially generating irregular 3D point clouds from an application perspective, too. While GANs have various applications if applied to images it is not obvious how artificially created irregular 3D point clouds can be useful. Although the theoretical insights presented in the paper are exciting, a more high-level motivation would further improve its quality. 4) A discussion of shortcomings of the presented method seems missing. While it is understandable that emphasis is put on novelty and its advantages, it would be interesting to see where the authors see room for improvement.