Outdoor Environment Reconstruction with Deep Learning on Radio
  Propagation Paths
Hrant Khachatrian Rafayel Mkrtchyan Theofanis P. Raptis
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.