Autoencoder Models for Point Cloud Environmental Synthesis from WiFi Channel State Information: A Preliminary Study

--This paper introduces a deep learning framework for generating point clouds from WiFi Channel State Information data. We employ a two-stage autoencoder approach: a PointNet autoencoder with convolutional layers for point cloud generation, and a Convolutional Neural Network autoencoder to map CSI data to a matching latent space. By aligning these latent spaces, our method enables accurate environmental point cloud reconstruction from WiFi data. HE proliferation of wireless communication technologies has led to an increased interest in using WiFi signals for various sensing applications. Among these, Channel State Information (CSI) data from WiFi signals provides rich information about the environment, making it a valuable resource for tasks such as indoor localization [1], [2], activity recognition [3], [4], and environmental mapping [5], [6].