  Point clouds provide a flexible geometric representation suitable for
countless applications in computer graphics; they also comprise the raw output
of most 3D data acquisition devices. While hand-designed features on point
clouds have long been proposed in graphics and vision, however, the recent
overwhelming success of convolutional neural networks (CNNs) for image analysis
suggests the value of adapting insight from CNN to the point cloud world. Point
clouds inherently lack topological information so designing a model to recover
topology can enrich the representation power of point clouds. To this end, we
propose a new neural network module dubbed EdgeConv suitable for CNN-based
high-level tasks on point clouds including classification and segmentation.
EdgeConv acts on graphs dynamically computed in each layer of the network. It
is differentiable and can be plugged into existing architectures. Compared to
existing modules operating in extrinsic space or treating each point
independently, EdgeConv has several appealing properties: It incorporates local
neighborhood information; it can be stacked applied to learn global shape
properties; and in multi-layer systems affinity in feature space captures
semantic characteristics over potentially long distances in the original
embedding. We show the performance of our model on standard benchmarks
including ModelNet40, ShapeNetPart, and S3DIS.
