Conventional methods for outdoor environment reconstruction rely
predominantly on vision-based techniques like photogrammetry and LiDAR, facing
limitations such as constrained coverage, susceptibility to environmental
conditions, and high computational and energy demands. These challenges are
particularly pronounced in applications like augmented reality navigation,
especially when integrated with wearable devices featuring constrained
computational resources and energy budgets. In response, this paper proposes a
novel approach harnessing ambient wireless signals for outdoor environment
reconstruction. By analyzing radio frequency (RF) data, the paper aims to
deduce the environmental characteristics and digitally reconstruct the outdoor
surroundings. Investigating the efficacy of selected deep learning (DL)
techniques on the synthetic RF dataset WAIR-D, the study endeavors to address
the research gap in this domain. Two DL-driven approaches are evaluated
(convolutional U-Net and CLIP+ based on vision transformers), with performance
assessed using metrics like intersection-over-union (IoU), Hausdorff distance,
and Chamfer distance. The results demonstrate promising performance of the
RF-based reconstruction method, paving the way towards lightweight and scalable
reconstruction solutions.

通过分析射频（RF）数据，本文提出了一种利用环境无线信号对室外环境进行重建的新方法，通过选定的深度学习技术在合成射频数据集 WAIR-D 上进行研究，评估了两种基于深度学习的方法，在性能评估中使用了交集 - 并集（IoU），豪斯多夫距离和切菲尔德距离等指标，结果显示基于射频的重建方法具有良好的性能，为轻量级和可扩展的重建解决方案铺平了道路。