VA-GCN: A point cloud analysis network used to mine local aggregation information

Creators: Hu, Haotian; Wang, Fanyi; Shen, Cheng

Abstract

In recent years, point cloud analysis models have made breakthroughs due to the development of local aggregation operators. In this article, we propose a new point cloud analysis network, Vector Attention Graph Convolution Network (VA-GCN) based on Vector Attention Convolution (VAConv) modules. VA-GCN can be easily embedded into other complex point cloud analysis models, which can promote the development and application of point cloud models in the field of artificial intelligence. In addition, we designed an open-source software for point cloud classification based on VA-GCN. Its ease of use and efficiency will allow non-professionals to quickly get started.

Additional Information

© 2021 The Author(s). Published by Elsevier Under a Creative Commons license - Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Received 11 August 2021, Revised 30 August 2021, Accepted 6 September 2021, Available online 17 September 2021. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). More information on the Reproducibility Badge Initiative is available at https://www.elsevier.com/physical-sciences-and-engineering/computer-science/journals.

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