使用深度图神经网络的无线3D点云交付
Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
摘要：
在典型的点云传送中，发送方使用基于八叉树的数字视频压缩来通过带限链路发送三维（3D）点和颜色属性。然而，基于数字的方案具有称为悬崖效应的问题，其中3D重建质量将是无线信道质量方面的阶跃函数。为了防止受到信道质量波动的悬崖效应，我们提出了称为HoloCast的软点云交付。虽然HoloCast根据无线信道质量实现了优雅的质量改善，但它需要较大的通信开销。在本文中，我们提出了一种新的方案，软点云交付，同时实现更好的质量和更低的通信开销。该方案引入了一个基于图神经网络（GNN）的端到端深度学习框架，以从无线衰落信道下的失真观测中重建高质量的点云。我们证明了所提出的基于GNN的方案可以通过去除衰落和噪声影响来重建干净的3D点云，并且开销很低。
英文摘要：
In typical point cloud delivery, a sender uses octree-based digital video compression to send three-dimensional (3D) points and color attributes over band-limited links. However, the digital-based schemes have an issue called the cliff effect, where the 3D reconstruction quality will be a step function in terms of wireless channel quality. To prevent the cliff effect subject to channel quality fluctuation, we have proposed soft point cloud delivery called HoloCast. Although the HoloCast realizes graceful quality improvement according to wireless channel quality, it requires large communication overheads. In this paper, we propose a novel scheme for soft point cloud delivery to simultaneously realize better quality and lower communication overheads. The proposed scheme introduces an end-to-end deep learning framework based on graph neural network (GNN) to reconstruct high-quality point clouds from its distorted observation under wireless fading channels. We demonstrate that the proposed GNN-based scheme can reconstruct clean 3D point cloud with low overheads by removing fading and noise effects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
