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Dynamic Graph CNN for Learning on Point Clouds

Authors:

Sanjay E. Sarma,

Michael M. Bronstein,

Justin M. SolomonAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 38, Issue 5

Article No.: 146, Pages 1 - 12

https://doi.org/10.1145/3326362

Published: 10 October 2019 Publication History

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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.

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Index Terms

Dynamic Graph CNN for Learning on Point Clouds
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Point-based models
      2. Shape analysis
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 38, Issue 5

October 2019

191 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3341165

Editor:
Marc Alexa
TU Berlin, Germany

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Copyright © 2019 ACM.

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Publication History

Published: 10 October 2019

Accepted: 01 June 2019

Revised: 01 May 2019

Received: 01 January 2019

Published in TOG Volume 38, Issue 5

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Air Force Office of Scientific Research
Toyota-CSAIL Joint Research Center
National Science Foundation
Amazon Research Award
MITIBM Watson AI Laboratory
Skoltech-MIT Next Generation Program
Army Research Office
ERC Consolidator
Google Faculty Research Award

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https://doi.org/10.1016/j.patcog.2024.111042
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https://doi.org/10.1016/j.patcog.2024.110997
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