Vision Reimagined: AI-Powered Breakthroughs in WiFi Indoor Imaging
- URL: http://arxiv.org/abs/2401.04317v1
- Date: Tue, 9 Jan 2024 02:20:30 GMT
- Title: Vision Reimagined: AI-Powered Breakthroughs in WiFi Indoor Imaging
- Authors: Jianyang Shi, Bowen Zhang, Amartansh Dubey, Ross Murch and Liwen Jing
- Abstract summary: WiFi as an omnipresent signal is a promising candidate for carrying out passive imaging and synchronizing the up-to-date information to all connected devices.
This is the first research work to consider WiFi indoor imaging as a multi-modal image generation task that converts the measured WiFi power into a high-resolution indoor image.
Our proposed WiFi-GEN network achieves a shape reconstruction accuracy that is 275% of that achieved by physical model-based methods.
- Score: 4.236383297604285
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Indoor imaging is a critical task for robotics and internet-of-things. WiFi
as an omnipresent signal is a promising candidate for carrying out passive
imaging and synchronizing the up-to-date information to all connected devices.
This is the first research work to consider WiFi indoor imaging as a
multi-modal image generation task that converts the measured WiFi power into a
high-resolution indoor image. Our proposed WiFi-GEN network achieves a shape
reconstruction accuracy that is 275% of that achieved by physical model-based
inversion methods. Additionally, the Frechet Inception Distance score has been
significantly reduced by 82%. To examine the effectiveness of models for this
task, the first large-scale dataset is released containing 80,000 pairs of WiFi
signal and imaging target. Our model absorbs challenges for the model-based
methods including the non-linearity, ill-posedness and non-certainty into
massive parameters of our generative AI network. The network is also designed
to best fit measured WiFi signals and the desired imaging output. For
reproducibility, we will release the data and code upon acceptance.
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