[2018] Dynamic Graph CNN for Learning on Point Clouds

[YoshiProton]

[Title] Dynamic Graph CNN for Learning on Point Clouds
[Authors] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon
(Submitted on 24 Jan 2018 (v1), last revised 11 Jun 2019 (this version, v2))
[ArXiv] arXiv:1801.07829 [cs.CV]

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.