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A task-driven sampling method based on graph convolution for 3D point cloud compression
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  • Yakun Yang,
  • Anhong Wang,
  • Donghan Bu,
  • Hao Jing
Yakun Yang
Taiyuan University of Science and Technology

Corresponding Author:[email protected]

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Anhong Wang
Taiyuan University of Science and Technology
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Donghan Bu
Taiyuan University of Science and Technology
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Hao Jing
Taiyuan University of Science and Technology
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Abstract

The previous point cloud compression methods only consider reducing the amount of data. However, in applications such as autonomous driving, the compression methods not only require smooth transmission, but also improve the efficiency of downstream tasks. To this end, we propose a task-driven sampling network based on graph convolution to achieve point cloud compression and recovery. First, we present a task-driven downsampling network based on graph convolution to compress the point cloud. Then, we present an upsampling network based on graph convolution to enhance and recover the point cloud. In order to optimize the compressed point cloud for task, we add the task loss to loss function for end-to-end training. Experiments for point cloud classification task on ModelNet40 dataset show that the compressed point cloud obtained through our network can achieve higher classification accuracy compared to other similar methods, and the reconstructed point cloud can further improve classification accuracy.
27 Oct 2023Submitted to Electronics Letters
27 Oct 2023Submission Checks Completed
27 Oct 2023Assigned to Editor
02 Nov 2023Reviewer(s) Assigned