Autoencoder Models for Point Cloud Environmental Synthesis from WiFi Channel State Information: A Preliminary Study
This work addresses wireless sensing and environmental mapping applications, but it appears incremental as it builds on existing autoencoder and PointNet methods.
The paper tackles the problem of generating environmental point clouds from WiFi Channel State Information data using a two-stage autoencoder framework, achieving accurate reconstruction as validated by experiments.
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. Experimental results validate the effectiveness of our approach, highlighting its potential for wireless sensing and environmental mapping applications.