Review for NeurIPS paper: Primal-Dual Mesh Convolutional Neural Networks

Additional Feedback: Overall, I tend to reject this paper (5 leaning towards 4) for two reasons: 1. that there is very limited novelty, using the model proposed in [20] and the features proposed in [12]. I will be willing to adjust my rating if the authors could convince me that their method is actually critical to the performance increases (this invalidates 2. while making 1. a much lesser concern). Additional comments: - I am not sure if the proposed architecture leverages any properties of a (manifold or not) mesh. It seems that this primal-dual formulation is applicable to any graphs, despite that the fact that some operations might be equivalent to certain mesh operations. How is the method different from graph convolutional networks on arbitrary graphs?