  Convolutional Neural Networks (CNNs) achieve impressive performance in a wide
variety of fields. Their success benefited from a massive boost when very deep
CNN models were able to be reliably trained. Despite their merits, CNNs fail to
properly address problems with non-Euclidean data. To overcome this challenge,
Graph Convolutional Networks (GCNs) build graphs to represent non-Euclidean
data, borrow concepts from CNNs, and apply them in training. GCNs show
promising results, but they are usually limited to very shallow models due to
the vanishing gradient problem. As a result, most state-of-the-art GCN models
are no deeper than 3 or 4 layers. In this work, we present new ways to
successfully train very deep GCNs. We do this by borrowing concepts from CNNs,
specifically residual/dense connections and dilated convolutions, and adapting
them to GCN architectures. Extensive experiments show the positive effect of
these deep GCN frameworks. Finally, we use these new concepts to build a very
deep 56-layer GCN, and show how it significantly boosts performance (+3.7% mIoU
over state-of-the-art) in the task of point cloud semantic segmentation. We
believe that the community can greatly benefit from this work, as it opens up
many opportunities for advancing GCN-based research.
