论文标题
无线3D点云使用深图神经网络传递
Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
论文作者
论文摘要
在典型的Point Cloud交付中，发件人使用基于OCTREE的数字视频压缩来在带限制的链接上发送三维（3D）点和颜色属性。但是，基于数字的方案有一个称为悬崖效应的问题，在无线通道质量方面，3D重建质量将是一个步骤功能。为了防止悬崖效应受到通道质量波动的影响，我们提出了称为全新的软点云传递。尽管Holocast根据无线通道质量实现了优雅的质量改进，但它需要大量的沟通开销。在本文中，我们提出了一个新颖的方案，以同时实现更好的质量和较低的沟通开销。提出的方案引入了基于图神经网络（GNN）的端到端深度学习框架，以从无线褪色通道下的扭曲观察结果中重建高质量的点云。我们证明，提出的基于GNN的方案可以通过消除褪色和噪声效应来重建清洁的3D点云，而低开销。
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.
