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The ability to see through walls is a crucial need for special operations and security forces. Our previous research has demonstrated that centimeter wave (CMW) imaging system operated at around 5 GHz WiFi signal offers a low power solution with good range and penetration capabilities. However, the accuracy of the existing system in scene reconstruction was limited due to computational complexity. In this work, we aim to leverage deep learning (DL) based algorithms to design a scene reconstruction approach with significantly improved accuracy. We utilize the high-fidelity electromagnetic (EM) simulation tool, SABR (Shooting and Bouncing Ray), for RF (Radio Frequency) simulations across different scenes and sensor setups. The backbone of our approach is an encoder-decoder neural network. To accommodate the sparse distribution of transmitter and receiver locations in 3D space, we recognize that the transformer with position encoding is more suitable to be used as our building blocks, as opposed to convolution blocks whose receptive field is the neighborhood grid. Additionally, recognizing the sparse nature of point clouds, our decoder integrates sparse tensors and convolutions via the Minkowski Engine. This innovation in model design not only makes it memory-efficient but also supports higher resolution reconstructions and the utilization of deeper learning architectures. We notice that WiFi 3D scene reconstruction using DL technique is a relatively unexplored problem, and we demonstrate that we are able to reconstruct the scene with resolution close to Rayleigh limit. Our approach has great potential to allow scene reconstruction behind obstacles on low SWaP hardware. It has wide applications such as battlefield, security and surveillance to detect and locate threats, search and rescue mission for trapped or injured under rubble, debris, or even medical fields for remote diagnosis and/or treatment, etc.
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