{"title":"Semantic Communication for Efficient Point Cloud Transmission","authors":"Shangzhuo Xie, Qianqian Yang, Yuyi Sun, Tianxiao Han, Zhaohui Yang, Zhiguo Shi","doi":"arxiv-2409.03319","DOIUrl":null,"url":null,"abstract":"As three-dimensional acquisition technologies like LiDAR cameras advance, the\nneed for efficient transmission of 3D point clouds is becoming increasingly\nimportant. In this paper, we present a novel semantic communication (SemCom)\napproach for efficient 3D point cloud transmission. Different from existing\nmethods that rely on downsampling and feature extraction for compression, our\napproach utilizes a parallel structure to separately extract both global and\nlocal information from point clouds. This system is composed of five key\ncomponents: local semantic encoder, global semantic encoder, channel encoder,\nchannel decoder, and semantic decoder. Our numerical results indicate that this\napproach surpasses both the traditional Octree compression methodology and\nalternative deep learning-based strategies in terms of reconstruction quality.\nMoreover, our system is capable of achieving high-quality point cloud\nreconstruction under adverse channel conditions, specifically maintaining a\nreconstruction quality of over 37dB even with severe channel noise.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Emerging Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.03319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
As three-dimensional acquisition technologies like LiDAR cameras advance, the
need for efficient transmission of 3D point clouds is becoming increasingly
important. In this paper, we present a novel semantic communication (SemCom)
approach for efficient 3D point cloud transmission. Different from existing
methods that rely on downsampling and feature extraction for compression, our
approach utilizes a parallel structure to separately extract both global and
local information from point clouds. This system is composed of five key
components: local semantic encoder, global semantic encoder, channel encoder,
channel decoder, and semantic decoder. Our numerical results indicate that this
approach surpasses both the traditional Octree compression methodology and
alternative deep learning-based strategies in terms of reconstruction quality.
Moreover, our system is capable of achieving high-quality point cloud
reconstruction under adverse channel conditions, specifically maintaining a
reconstruction quality of over 37dB even with severe channel noise.