Soft 2D-to-3D Delivery Using Deep Graph Neural Networks for Holographic-Type Communication
Takuya Fujihashi, Toshiaki Koike-Akino, Takashi Watanabe
Abstract
Holographic-type communication, i.e., three-dimensional (3D) content delivery, will be a crucial application for modern wireless and mobile networks. In this paper, we propose a novel soft delivery scheme to realize efficient 3D content delivery. Specifically, the proposed scheme sends a single 2D image over error-prone wireless channels using discrete cosine transform followed by near-analog modulation. At the receiver, a 2D-to-3D decoder based on graph neural networks (GNN) reconstructs the corresponding 3D point cloud and mesh from the received 2D image. We verify that the proposed soft 2D-to-3D delivery scheme can reconstruct clean 3D data gracefully from the soft-delivered 2D image even in the presence of fading and noise distortion. In addition, the proposed scheme can generate higher-quality 3D data compared with direct 3D content delivery schemes.
BibTeX
@inproceedings{icassp2023_soft2dto3ddelive,
title = {Soft 2D-to-3D Delivery Using Deep Graph Neural Networks for Holographic-Type Communication},
author = {Takuya Fujihashi and Toshiaki Koike-Akino and Takashi Watanabe},
booktitle = {ICASSP 2023},
year = {2023}
}