. Point cloud segmentation using the proposed neural network. Bottom: schematic neural network architecture. Top: Structure of the feature spaces produced at different layers of the network, visualized as the distance from the red point to all the rest of the points (shown left-to-right are the input and layers 1-3; rightmost figure shows the resulting segmentation). Observe how the feature space structure in deeper layers captures semantically similar structures such as wings, fuselage, or turbines, despite a large distance between them in the original input space. Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. 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 highlevel 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.
To cope with current limitations of point cloud representation methods, Wang et al. REF propose a graph representation for point clouds and show improved performances in classification and segmentation tasks.
Dynamic Graph CNN for Learning on Point Clouds
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