Point Cloud in the Air
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909058742616064 |
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| author | Shao, Yulin Bian, Chenghong Yang, Li Yang, Qianqian Zhang, Zhaoyang Gunduz, Deniz |
| author_facet | Shao, Yulin Bian, Chenghong Yang, Li Yang, Qianqian Zhang, Zhaoyang Gunduz, Deniz |
| contents | Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality. In most scenarios, PCs acquired by remote sensors must be transmitted to an edge server for fusion, segmentation, or inference. Wireless transmission of PCs not only puts on increased burden on the already congested wireless spectrum, but also confronts a unique set of challenges arising from the irregular and unstructured nature of PCs. In this paper, we meticulously delineate these challenges and offer a comprehensive examination of existing solutions while candidly acknowledging their inherent limitations. In response to these intricacies, we proffer four pragmatic solution frameworks, spanning advanced techniques, hybrid schemes, and distributed data aggregation approaches. In doing so, our goal is to chart a path toward efficient, reliable, and low-latency wireless PC transmission. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00658 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Point Cloud in the Air Shao, Yulin Bian, Chenghong Yang, Li Yang, Qianqian Zhang, Zhaoyang Gunduz, Deniz Information Theory Machine Learning Multimedia Signal Processing Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality. In most scenarios, PCs acquired by remote sensors must be transmitted to an edge server for fusion, segmentation, or inference. Wireless transmission of PCs not only puts on increased burden on the already congested wireless spectrum, but also confronts a unique set of challenges arising from the irregular and unstructured nature of PCs. In this paper, we meticulously delineate these challenges and offer a comprehensive examination of existing solutions while candidly acknowledging their inherent limitations. In response to these intricacies, we proffer four pragmatic solution frameworks, spanning advanced techniques, hybrid schemes, and distributed data aggregation approaches. In doing so, our goal is to chart a path toward efficient, reliable, and low-latency wireless PC transmission. |
| title | Point Cloud in the Air |
| topic | Information Theory Machine Learning Multimedia Signal Processing |
| url | https://arxiv.org/abs/2401.00658 |