Reference-Free 3D WiFi AP Localization by Outdoor-to-Indoor Bridging

WiFi access point (AP) localization is essential for wireless infrastructure management and location-based services. While deep learning approaches have shown promising accuracy improvements, they require extensive training data with precise coordinates, making large-scale deployment impractical. Traditional localization techniques also rely heavily on indoor reference points (RPs), resulting in costly and labor-intensive deployments. We present WiSight, a novel framework that eliminates the need for indoor RPs by leveraging GPS-tagged outdoor RSS measurements and 3D building geometry, anchoring the indoor reference frame to the global coordinate system using GPS-tagged exterior points. WiSight first identifies virtual anchor positions on building exteriors through outdoor signal propagation modeling, then reconstructs indoor AP configurations using unlabeled RSS measurement pairs and multidimensional scaling. Extensive evaluation across multiple buildings demonstrates that WiSight achieves an average 3D AP localization error of 7.1 m (median: 6.8 m), reducing error by 59% compared to an opportunistic GPS-based approach. In office environments, WiSight attains 9.6 m error (median: 8.5 m)—22% lower than the state-of-the-art deep learning-based method, while achieving 82% floor-level accuracy without requiring any indoor RPs or training data.