Abstract

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.

Keywords

Point cloudComputer scienceRepresentation (politics)Convolutional neural networkEmbeddingComputer graphicsGraphSegmentationGraphicsFeature (linguistics)Point (geometry)Artificial intelligenceDifferentiable functionFeature learningTopology (electrical circuits)Theoretical computer scienceComputer graphics (images)

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Publication Info

Year
2019
Type
article
Volume
38
Issue
5
Pages
1-12
Citations
6107
Access
Closed

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Citation Metrics

6107
OpenAlex
1217
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Cite This

Yue Wang, Yongbin Sun, Ziwei Liu et al. (2019). Dynamic Graph CNN for Learning on Point Clouds. ACM Transactions on Graphics , 38 (5) , 1-12. https://doi.org/10.1145/3326362

Identifiers

DOI
10.1145/3326362
arXiv
1801.07829

Data Quality

Data completeness: 88%