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\begin{document}

\title{A task-driven sampling method based on graph convolution for 3D point
cloud compression}



\author[1]{Yakun Yang}%
\author[1]{Anhong Wang}%
\author[1]{Donghan Bu}%
\author[1]{Hao  Jing}%
\affil[1]{Taiyuan University of Science and Technology}%


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  \date{May 26, 2024}


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\begin{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.%
\end{abstract}%



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\textbf{Hosted file}

\verb`A task-driven sampling method based on graph convolution for 3D point cloud compression.docx`  available at \url{https://authorea.com/users/678086/articles/674849-a-task-driven-sampling-method-based-on-graph-convolution-for-3d-point-cloud-compression}

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