DoRF++: Spherical Representation Learning over Doppler Radiance Fields for Robust Wi-Fi Sensing

• URL: http://arxiv.org/abs/2608.08381v1
• Date: Sun, 09 Aug 2026 00:23:55 GMT
• Title: DoRF++: Spherical Representation Learning over Doppler Radiance Fields for Robust Wi-Fi Sensing
• Abstract summary: Doppler Radiance Fields (DoRF) models Doppler velocity projections extracted from Wi-Fi Channel State Information (CSI)<n>DoRF++ is a spherical-learning design that applies spherical Transformers for activity classification.<n>Experiments on our collected hand-gesture dataset show that DoRF++ significantly outperforms state-of-the-art Wi-Fi-based HAR methods in cross-user generalization accuracy.
• Score: 20.859205920322676
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Motivated by the IEEE 802.11bf effort to standardize advanced WLAN sensing, interest in Wi-Fi Channel State Information (CSI) for passive, device-free, and privacy-preserving activity and gesture recognition has grown rapidly. Recent studies have shown that Doppler velocity projections extracted from CSI, which directly reflect human-motion velocity, enable more robust human activity recognition (HAR) and stronger generalization across users and unseen conditions. Nevertheless, reliable generalization under real-world variability remains a major challenge, hindering the adoption of Wi-Fi sensing in real-world applications. To address this challenge, we introduce Doppler Radiance Fields (DoRF), bringing the concept of neural radiance fields (NeRF) from computer vision into Wi-Fi sensing. DoRF models Doppler velocity projections extracted from Wi-Fi CSI as sparse and diverse virtual-camera views of human motion. It then infers a latent 3D motion sequence whose projections along learned effective Doppler directions explain the CSI-derived Doppler observations. The recovered motion is subsequently projected onto an equiangular grid of directions on the unit sphere, producing a spherical representation of the underlying motion. Since DoRF naturally defines the Doppler representation on spheres, we further introduce DoRF++, a spherical-learning design that applies spherical Transformers for activity classification. Experiments on our collected hand-gesture dataset show that DoRF++ significantly outperforms state-of-the-art Wi-Fi-based HAR methods in cross-user generalization accuracy, especially for difficult gestures in settings with a single multi-antenna receiver access point (AP).

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