# DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi

arXiv: https://arxiv.org/abs/2507.12132 · HTML: https://mlsanity.com/paper/2507.12132/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points](https://mlsanity.com/paper/2103.09924/) (2021)
- [A Tutorial-cum-Survey on Self-Supervised Learning for Wi-Fi Sensing: Trends, Challenges, and Outlook](https://mlsanity.com/paper/2506.12052/) (2025)
- [FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning](https://mlsanity.com/paper/2203.02014/) (2022)
- [Decimeter Ranging with Channel State Information](https://mlsanity.com/paper/1902.09652/) (2019)
- [LiteHAR: Lightweight Human Activity Recognition from WiFi Signals with Random Convolution Kernels](https://mlsanity.com/paper/2201.09310/) (2022)
- [Joint Human Orientation-Activity Recognition Using WiFi Signals for Human-Machine Interaction](https://mlsanity.com/paper/2210.05078/) (2022)
- [MELON: NeRF with Unposed Images in SO(3)](https://mlsanity.com/paper/2303.08096/) (2023)
- [MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition](https://mlsanity.com/paper/2506.12997/) (2025)

## Built on by (most cited first, 3 of 3)

- [Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition](https://mlsanity.com/paper/2509.15129/) (2025)
- [WiFuse: An Attention Mechanism for Human Activity Recognition using Fused CSI Amplitude and Delay-Doppler Channel Features](https://mlsanity.com/paper/2608.00642/) (2026)
- [DoRF++: Spherical Representation Learning over Doppler Radiance Fields for Robust Wi-Fi Sensing](https://mlsanity.com/paper/2608.08381/) (2026)

## Abstract (arXiv)

Wi-Fi Channel State Information (CSI) has gained increasing interest for remote sensing applications. Recent studies show that Doppler velocity projections extracted from CSI can enable human activity recognition (HAR) that is robust to environmental changes and generalizes to new users. However, despite these advances, generalizability still remains insufficient for practical deployment. Inspired by neural radiance fields (NeRF), which learn a volumetric representation of a 3D scene from 2D images, this work proposes a novel approach to reconstruct an informative 3D latent motion representation from one-dimensional Doppler velocity projections extracted from Wi-Fi CSI. The resulting latent representation is then used to construct a uniform Doppler radiance field (DoRF) of the motion, providing a comprehensive view of the performed activity and improving the robustness to environmental variability. The results show that the proposed approach noticeably enhances the generalization accuracy of Wi-Fi-based HAR, highlighting the strong potential of DoRFs for practical sensing applications.