Title: GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals
Venue: NeurIPS 2025
Accepted: yes
Keywords: Radio Frequency

Radar

Multi-view Reconstruction

Neural Rendering
Abstract: GeRaF is the first method to use neural implicit learning for near-range 3D geometry reconstruction from radio frequency (RF) signals. Unlike RGB or LiDAR-based methods, RF sensing can see through occlusion but suffers from low resolution and noise due to its lens-less imaging nature. While lenses in RGB imaging constrain sampling to 1D rays, RF signals propagate through the entire space, introducing significant noise and leading to cubic complexity in volumetric rendering. Moreover, RF signals interact with surfaces via specular reflections requiring fundamentally different modeling. To address these challenges, GeRaF (1) introduces filter-based rendering to suppress irrelevant signals, (2) implements a physics-based RF volumetric rendering pipeline, and (3) proposes a novel lens-less sampling and lens-less alpha blending strategy that makes full-space sampling feasible during training. By learning signed distance functions, reflectiveness, and signal power through MLPs and trainable parameters, GeRaF takes the first step towards reconstructing millimeter-level geometry from RF signals in real-world settings.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper's goal is near-range 3D geometry reconstruction from radio frequency signals. The authors challenge low SNR, and high computational frequency due to lensless imaging and propose novel RF volume rendering equations for different ray propagation physics of radio waves unlike visible light.\nGeRaF framework consists of an SDF network and a reflective network. Each predicts geometry (volume density and normals) and reflectiveness. The authors assume every 3D point in space is a potential reflector, formulating the received signal as a sum of phase-shifted signals over sampled points(signal tracing). The amplitude of the signal is determined by the reflection direction computed from normal, the reflectivity of the 3D point, and squared transmittance due to the round-trip path together with effective input amplitude which is learned as a global parameter.\nTo render a lensless image in reduced complexity, the authors devise the lensless alpha blending strategy and signal tracing bank, This strategy utilizes primary ray direction to sample points and blending. Authors show that ray-dependency in transmittance and alpha computation can be safely removed and the use of primary ray direction can reduce redundant computation. The authors also adopt a memory bank mechanism that reuses the signal tracing results of previous iterations. Lastly, the authors proposed dynamic loss masking for loss backpropagation on valid signals.\nCompared to the Match Filter baseline, GeRaF shows higher geometry reconstruction accuracy in both line-of-sight and non-line-of-sight.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n1. The authors are the first to suggest a 3D reconstruction of an object in a near-range with radio frequency signals. This enables the reconstruction of an object in non-line-of-sight, which is a promising application.\n2. This paper formulated a lensless neural rendering framework for RF signal images. The authors devised a strategy to compute computationally complex forward and backward operations of lensless imaging by removing the necessity of ray-direction dependency in the computation pipeline. The authors clearly derive their novel imaging formulation in detail\n3. The computational complexity analysis graph shows that the latency and memory capacity are greatly reduced by author’s proposals.\n\nWeakness.\n1. Due to the absence of an existing public dataset on near-range radio frequency scanning and precedent works, the evaluation of the method is limited to the dataset captured by authors, and comparisons are conducted only with the Matched Filter baseline.\n\nQUESTIONS:\n1. The main contribution of this work would be the validity of radar physics formulation. To see the validity of the model, can authors also include the ablation in the other scenes as well? (Section 2.6 of supplementary material)\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes a novel differentiable rendering method for reconstructing high-resolution geometry using radio frequency (RF) signals. GeRaF introduces three important techniques: lensless sampling, physics-based RF volumetric rendering, and a lensless alpha blending strategy. As a result, GeRaF reduces computational cost while maintaining the high-quality resolution of the baselines. The learnable part of the pipeline drops from cubic complexity to linear per iteration, slashing GPU memory.\n\nSTRENGTHS AND WEAKNESSES:\n- Strengths\n   - GeRaF extends NeRF-style learning to raw mmWave radar, enabling 3-D reconstruction even when the target is hidden behind thin occluders—something RGB/LiDAR can’t do.\n   - Sharing density/transmittance across antennas and re-using a signal-tracing bank cuts the learnable complexity from $O(N_{\\text{ant}}N_{\\text{ray}}N_z)$ to $O(N_{\\text{ray}}N_z)$.\n   - Quantitative and visualization results outperform the matched filter (MF) method.\n\n- Weaknesses\n   - If a surface patch never reflects toward the radar in any scan, the network has no evidence and cannot hallucinate that geometry. Robust scanning strategies or priors are still needed.\n   - GeRaF has three models: SDF network, Reflective network, and Signal power prediction. While training the three models, is there a risk that the exploration error could become disproportionately large in one of them, leading to instability? If so, how can this issue be mitigated?\n\nQUESTIONS:\nWhile I am unfamiliar with the area of this proposed work, I would like to raise a few questions.\n\n1. Could you clarify whether the authors considered any baselines beyond the matched-filter (MF) approach?\n2. In Figure 6, I think it is still hard to recognize the objects in the experimental results. Nevertheless, what specific advantages does GeRaF offer? For example, does it reconstruct certain features or regions particularly well compared to the baselines?\n\nLIMITATIONS:\nYes, the authors clearly address the limitations and potential negative societal impact of GeRaF.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper presents a novel method for mesh reconstruction using radio frequency (RF) information, addressing occlusion challenges. The authors propose a neural rendering framework incorporating a Matched Filter (MF) and lensless sampling to model physics-based RF volume rendering. Qualitative and quantitative results demonstrate the method’s ability to reconstruct objects reasonably well, even under unseen lighting conditions.

STRENGTHS AND WEAKNESSES:
[Strengths]

- This paper proposes the first RF-based object-level mesh reconstruction method with occlusion handling, unlike prior RF works focusing on large-scale environments and novel view synthesis.
- The designed network is reasonable to me: The use of MLPs for SDF and reflectance networks, combined with MF image supervision and lensless sampling, efficiently reduces computational overhead.

[Weaknesses]

- While the authors use iPhone-scanned meshes as ground truth, the visualizations reveal noticeable inaccuracies in these reference meshes. This raises concerns about the reliability of both qualitative and quantitative evaluations, as the baseline itself may be flawed.
- The reconstructed mesh from MF images and GeRaF both lack detail, and the 32-hour training time per object raises concerns about scalability. The method’s real-world applicability is unclear. Suspicious package detection may be used by other efficient methods.

QUESTIONS:
The paper states that the method works best for metallic objects. If the occluding object itself (e.g., the box) is metallic, would this introduce additional interference or artifacts in the reconstruction? Could the authors clarify how the system handles such cases?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 4
DATE: 2025-07-03