GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals
Authors: Jiachen Lu
Hailan Shanbhag
Haitham Al Hassanieh
Venue: NeurIPS 2025 spotlight ()
Keywords: Radio Frequency
Radar
Multi-view Reconstruction
Neural Rendering
Primary area: applications
TL;DR: GeRaF is the first method to use neural implicit learning for near-range 3D geometry reconstruction from radio frequency (RF) signals.

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.

GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals
Jiachen Lu", Hailan Shanbhag', Haitham AI Hassanieh
École Polytechnique Fédérale de Lausanne (EPFL)
Abstract
GeRaF is the first method to use neural implicit learning for near-range 3D geome- try reconstruction from radio frequency (RF) signals. Unlike RGB or LiDAR-bases methods, RF sensing can see through occlusion but suffers from low resolution and noise due its lensless imaging nature. While lenses RGB imaging constrain sampling ID rays, RF signals propagate through the entire space, introducing sig- nificant 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 suppress irrelevant signals, (2) implements physics-based RF volu- metric rendering pipeline, and (3) proposes a novel lensless sampling and lensless 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-leve geometry from RF signals in real-world settings.
Introduction
Geometry reconstruction
fundamental problem that enables wide range of applications in fields such as virtual reality [14 21] and robotics [22] In recent years, neural reconstruction methods [55] ST
35 have gained significant attention. key advantage these methods is their ability to represent the geometry of scene continuously, which can help in many down- stream tasks. However, vision-based sensors often struggle in environments with adverse weather conditions 67
40
41
20
37
34 or even become completely unusable when objects are obscured by
occlusions
65
66
12 13.
In contrast, radio frequency (RF) sensing, specifically wireless millimeter-wave (mm Wave) sensing, has the ability see through occlusions and remains robust under challenging visibility conditions and, unlike X-Ray. is not dangerous to humans [56]. making it a compelling alternative for 3D reconstruction. This could open up plethora of applications; for example, seeing whether items inside a box are the correct items, are damaged, or pose a threat, without having to ever open up the box. On the other hand, mmWave resolution extremely low compared to vision. Fig. [ shows an example of one radar image, which shows significantly less visual context than camera images provide, making it hard to directly extract 3D reconstruction from a radar image alone. In order to perform more complete 3D reconstruction, some works 2
57 13 have done 3D point cloud reconstruction or human body tracking by leveraging movement or emulating larger antenna apertures, however, the recovered point clouds from RF are still too sparse and noisy to reliably recover detailed geometry. Other works 6 24 have proposed using neural implicit representations for RF sensing, aiming concentrate scene information from heavy noise through neural optimization.
65
30
"Co-primary first authors, indicates equal contribution.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

However, their systems are designed for large- scale environments and are far from achieving millimeter-level precision. Compared to near- field surface reconstruction, propagation charae- teristics of RF signals change dramatically when objects are close to the radar [5), meaning processing methods for large scale scenes, such as beamforming [5], introduces a considerable amount of distortion and noise, often exceeding the noise acceptable for gradient descent leading to unstable optimization.
Object inside
Scan
Radar
Camera
Multiview
Seaminng
In this paper, we propose the first method to overcome the fundamental dilemma between massive RF noise in the input and millimeter- level geometry reconstruction by using neural representation learning, shown in Fig. I Achiev- ing this goal, however, is not so straightforward. Using RF imaging directly to infer surface ge- ometry presents fundamentally different chal- lenges from traditional image-bace based approaches, in three main ways. (1) A key challenge is the absence of a lens-based imaging model in wire- less systems. While vision rendering uses lenses to filter and focus relevant light rays, wireless systems operate via lensless imaging, capturing all incoming RF signals without directional fil- tering. This leads low signal-to-noise ratios, as irrelevant signals cannot be excluded. (2) There is fundamental difference in the propagation characteristics between RF signals and visible light. While visible light propagation is predominantly determined by scattering, RF reflections are dominated by specular reflections. Therefore. volumetric rendering techniques that are designed for scattering- vision sensors are unsuitable for RF imaging.
Planes
Radar
Ours
Figure I: GeRaF is the first method to reconstruct millimeterlevel geometry of non-line-of-sight ob- jects using radio frequency (RF) signals by us- ing neural implicit representations. Right, shows the radar heatmap from one scan, a camera scan, the surface directly reconstructed from the radar heatmap images, and surface output from GeRaF.
(3) Finally, a major challenge the high computational cost of lensless volumetric rendering. Unlike lens-based rendering, which samples along 1D) rays, lensless rendering requires sampling across the full 3D space, leading to cubic complexity, making naïve implementations intractable.
To address the outlined challenges, we propose the following key contributions: (1) We perform geometry reconstruction by introducing a matched filter (MF) into the framework that discards irrelevant signals and preserves the most informative ones. (2) We implement physics-based RF volumetric rendering, which integrates a RF-specific reflection model and incorporates a mathe- matical framework for specular reflections directly into the rendering process. (3) We propose novel lensless sampling and lensless alpha blending strategy that enables comprehensive scene coverage while significantly reducing computational cost, making volumetric rendering feasible for millimeter-level RF applications. Building upon these components, we design our neural model with three key sub-networks: Signed Distance Function Network, Reflective Network, and Signal Power Prediction, which are implemented with MLPs or trainable parameters.
We evaluate our system using 77 GHz mm Wave radar mounted on robotic arm, scanning variety of real-world objects. Our results demonstrate that GeRaF takes a significant first step toward accurate 3D reconstruction from RF signals, outperforming currently adopted methods in both reconstruction quality and robustness to experimental noise. These findings highlight GeRaF's ability to infer more detailed scene geometry, as compared current methods, even under challenging sensing conditions.
2 Related Work
Vision-Based Multi-View 3D Reconstruction and Inverse Rendering Traditional methods exploit photometric consistency across images 17 and fuse the resulting depth maps into a dense point
cloud [16 63, 58) To obtain surface representation, techniques such as Alpha Shapes [15] and
Poisson Surface Reconstruction (27 are commonly applied as post-processing steps on the point
cloud. With the rise of deep learning, methods like Neural Radiance Fields (NeRF) 39 and Gaussian

---

Tritnmittdd Sprool
Ampihudd
Relketeed Signal
rellectoo
8
6
Tame
a) FMCW amplitude
b) Transmit Refect Signal
Specular Reflections
Diffuse Reflections
c) Radar Reflections
Figure 2: (a) Frequency Modulated Continuous Waveform, (b) the reflected signal is delayed version of the transmitted signal, to resolve distance. (c) Specular reflections dominate radar wave propagation (d) diffused reflections that dominate vision, (c) the radar reflections modeled to incorporate specular reflections and signal spread.
Splatting (3DGS) [28] use learnable parameters to reconstruct 3D scenes from multi -iew images.
Building upon neural implicit representations 42 62 61 55] and 3DGS (3 25 33
18 g recent
work further decomposes scenes into explicit geometry and reconstruct surface meshes. Moreover, [9] [60] address the challenge of predicting lighting and material properties by considering complex light interactions, based on the explicit forward rendering equation with BRDF, a process known as inverse rendering. This technique more physically grounded and brings reconstructions closer to real-world appearance. While these methods typically operate on lens- based RGB images, [38] proposes novel view synthesis on lensless RGB images. However, lensless RGB images can still be mapped to a lens-based imaging model without altering the underlying ray physics, unlike RF signals, where such transformation is fundamentally invalid. To date, all of these 3D representations are derived from RGB images, which are not easily transferable to mm Wave data.
[46 77 55 (26
35
3D Radar Imaging Some works have used low frequency radars estimate the 3D pose of humans and track them through walls and occlusions 2 30 However, these works leverage human motion to combat specularity, and are geared towards human mesh reconstruction. Past work also leverages deep learning in the context of millimeter wave radar data for imaging or point cloud completion, however these works primarily focus the context of self drividi cars, where image resolution is not important and may not generalize to other objects [20 47 31
49
More recently, 36 68 9 take inspiration from NeRF 39 to solve different problem of estimating the wireless channel based on the predicted signal emitted from different reflectors in space. A group of work [4] (52) applies NeRF to simulated satellite images; [6] [24] tries to reconstruct 3D scenes by representing the wireless signal the frequency domain, and learning the reflectance and occupancy of different points. However, all of these prior works try to perform radar image rendering tailored for reconstructing large scale scenes such as streets or satellite images and don't 't address close range high resolution object reconstruction, which requires different wave propagation modeling explained in Appendix A.3 Authors of [50]. proposes a method for 3D neural reconstruction of objects. However, their evaluation is limited to simulated data, which when dealing with wireless signals, cannot come close to representing the complexity of real world experiments.
It is important distinction to make, that all prior works of 3D neural reconstruction using radio frequency are either limited to simulated data or reconstruction for far range scenes, whereas our work is the first paper to propose complete high resolution 3D mesh reconstruction.
3 Technical Background
Radar Basics A mm Wave radar works by transmitting a wireless signal and receiving back reflections that come from reflectors in the scene. It operates in the millimeter-wavelengee bands, and uses Frequency Modulated Continuous Wave (FMCW) and antenna arrays to help resolve spatial ambiguity. As shown in Fig. [a), the transmitted FMCW is a function that is linearly increasing in frequency over time. Considering a single reflector and a single antenna, the received chirp is simply time-delayed version of the transmitted chirp, Fig. (()). To resolve range ambiguity, the received chirp is multiplied with the conjugate of the transmitted chirp and can be expressed as a complex function:
frequency
(1) where A is the signal amplitude, d is the round-trip propagation distance, e is the speed of light, 7 = d/c the round-trip delay, f is the starting frequency, and & the chirp slope. For multiple reflectors, we simply receive the linear combination of all the reflections.
stt) A- 12-e- A- (f2nkr)) j2nfr
3

---

Lens-based
Lensless Imaging
Top Down View
Side View
Front View
(a) Lens-based vs lensless imaging
(b) Example MF image from measured radar data.
Figure 3: a) Comparison between lens-based imaging and lensless imaging models. b) Matched filter image of the metal bunny, summed along the vertical, horizontal, and depth axis.
Reflections Unlike light, whose wavelength (order of 10 meters) much smaller than surface inregularities. resultin in predominantly diffuuedd reflection, RF signals have much longer wavelengths (order of 10-3 meters). In this case, the majority surfaces appear relatively smooth to the RF signal, and specular reflections becomes more prominent than diffused scattering. For specular reflections, the angle of incidence the same as the angle of reflection, while diffused reflections tend to scatter different angles. The difference is illustrated in Fig. cc.d).
In this paper, we primarily expect specular reflections. We follow shifted Lambertian model where the power of the reflections that return to the receiver, is scaled according to the normal and the signals reflection angle, see Fig. 2 c). The received power is modulated by the directional scaling factor (w, wr), where (wo is the ideal specular vector and w, is the vector from the reflecting surface to the receiver (see Appendix A.2).. The reflection strength also depends on material properties and the thickness of the object, which model using reflective coefficient a.
additional power decay factor of (4πu)² divided from the received reflection amplitude ((4ru)* for power), where u is the distance between the point and antennas, accurately model the free space path loss (see Appendix A. Given an input signal with amplitude Axx. the received signal amplitude Arx can be expressed as:
Axx X (4Tu)² Axx (wo wr)
Lensless imaging Unlike traditional visual imaging systems that rely on lenses, radar imaging is inherently a lensless imaging technique. As illustrated in Fig. Ba| pinhole visual systems acts as a physical filter, allowing only one ray per scene point to reach the CMOS sensor while blocking unrelated rays. In photography, increasing the aperture size allows more light rays to enter, but this also leads to blur and noise in the image. As the aperture approaches an infinite size, the system transitions into a lensless imaging model. Lensless imaging, such as with radar antenna arrays, does not employ any physical pinhole-all all incoming rays are received by all antennas.
In contrast, radar imaging relies algorithmic processing reconstruct the scene. The core idea involves applying a digital filter to remove unrealistic signals and keep the most relevant ones, leveraging the time-varying and phase-delay information of the received signals. Each received signal, arriving via different path, carries distinct phase shift defined by its propagation delay. The algorithm used for this process is called the matched filter.
Differentiable Matched Filter This algorithm is implemented by correlating the received signal with an ideal reference signal. From Eq. | the reference signal for a single reflector and a single antenna is known. For multiple antenna measurements, we have:
Na
P(x)
Σ
Σ s(i, ef eakkt 12nsT
where Nm is the number of antennas, T; represent the round-trip delay corresponding to the i-th antenna, and sii t) denotes the received signal at time from the i-th antenna. We omit the transmitted amplitude Ag since it is constant. This process effectively averages out signals that are inconsistent with the hypothesized signal model, a matched filter (MF) power image shown in Fig. (3b
To enable integration into a differentiable rendering framework, we also compute the backpropa- gation of MF. Both the forward and backward passes are implemented in parallel on the GPU. All implementation details are provided in Appendix A.4 and A.5

---

value (())
Primary Ray
grad Vf(3)
Alpha Blending
Transmistance
Densiyy
mm Waye Radar
(ol, (0,)
Matched Filter
Reflectiveneess
Amplitude Tracing
enslees Alpha Blending Ava
Decay
Aperture
Reflective Network
Loss
Primary ray
Radar Lobe
Signal Power Prediction
Pazz ()
Arx (K) -in.et pott
Differentiable Matched Filter
Lensless Sampling
Pbysies-based RF' Volumetric Rendering
Figure 4: Overview of the GeRaF framework. (1) Lensless sampling replaces ray-based methods. (2) A neural implicit model predicts geometry, reflectivity, and power. (3) RF volumetric rendering simulates physical signal propagation. (4) Matched filtering produces MF power images (heatmaps). (5) An L2 loss compares the rendered and ground truth power for end-to-end training.
4
Overview
Given raw RF signals and the positions of all antennas across multiple antenna planes, GeRaF aims reconstruct the surface geometry of the object by predicting a signed distance function (SDF), without requiring any additional information. GeRaF consists of five main components, illustrated in Fig. 4 (1) We begin with point sampling. Unlike the we propose lensless sampling strategy that aligns with the physical nature of radar sensing. Details are provided in Sec. 6 (2) The sampled points are passed through three sub-networks to predict the scene's geometry, reflective properties, and radar signal power. (3) Using these predictions, along with surface normals (computed as gradients of the SDF), reflection directions, and power decay, we perform ahysics-based RF volumetric rendering for each antenna, as described in Sec. [ To correctly compute opacity and transmittance under the lensless setting, we introduce novel lensless alpha blending strategy. detailed Sec. 6 (4) Next, we apply a matched filter (MF), defined in Eq. E.2 to compute the reflected power each sampled point and generate the rendered MF heatmap. This step converts high-frequency time-domain signals into a lower-dimensional spatial representation of the scene. (5) Finally, apply L2 loss to minimize the discrepancy between the rendered heatmap and the ground truth, enabling end-to-end optimization of the entire framework via backpropagation.
sampling used in vision-based based methods (39
5 Physics-based RF Volumetric Rendering
We begin this section with simple ray-tracing-basee -ased sampling strategy. Assume we sample single ray emitted from receiver antenna located Xant the direction wr A point along the ray can then be represented as x Xant + ur, where is the sampling distance along the ray, and r is the unit direction vector opposite to wy. We first present Signal Trucing, which computes the frequency and phase shift of the signal. Then, we introduce Signal Amplitude Rendering, which "renders" the amplitude term of the received signal.
5.1 Signal Tracing
The Signal Tracing generates expected signal responses for all possible propagation paths based on hypothesized scene or spatial configuration. Simulating the received signal corresponds to modeling the received chirp at each individual antenna. Using the single-reflector expression in [q the reference signal for scene with multiple reflectors the superposition of contributions from all reflectors. However, in practice, there are no explicit "reflectors": instead, we are dealing scene. We treat every point x in computational space Ppps as a potential the surface of an object. Instead, volume
with a continuous reflector, regardless of whether it lies in free space density and reflective properties are encoded in the amplitude term Axx). The received signal is expressed in Eq. and is implemented in parallel on the GPU. Implementation details provided in
5

---

Appendix
A.4 and A.5.
s(
a(aant;))
Σ XED
Aa(x) e flakret e e
(4)
5.2 Signal Amplitude Tracing
The amplitude of the reflected radio frequency signal is influenced by several factors, including surface geometry, material reflectivity, reflection angle, and power decay due to propagation distance. Unlike vision-based NeRF rendering [39] radar sensing involves round-trip path, requiring us to model both the incoming ray from the transmitter and the outgoing ray to the receiver.
Given the opaque density [55] p(u), the accumulated transmittance along the incoming path can be computed ((u) exp (- p(u) du). However, due the round-trip path of radar signals, the effective accumulated transmittance becomes T ((n)? meaning that only fraction T(u)² of the signal remains after returning to the receiver.
By combining volumetric rendering, reflectivity, the reflection angle and power decay defined in 2 2 the amplitude the reflected signal at the receiver antenma (rx), assuming a symmetric return path along the same ray, is given by:
Axx (Xant, wr a(u) (wo w,) (u)) p(u) A dl,
where a(u) is the reflectivity at distance u, w. the outgoing direction computed as wa wi 2(n wi)n, with being the incoming direction and n the surface normal (obtained from the gradient of the SDF), y the power decay, and A the transmitted input power. The proportional constant, a, in Eq. Eq.2remmins fixed across all points on the ray. Therefore, without loss of generality, we absorb this constant into the transmitted amplitude term Axx, and define an effective transmitted amplitude A/c
Opaque density representation To represent volume density and accumulated transmittance, start from the SDF. The scene is defined by an SDF f: : R - R, which maps spatial location x R3 to its signed distance from the nearest surface of the object. This function is parameterized by a MLP. The object surface S is defined as the zero-level set of the SDF.
Following [55],
the S-density function (x)),,,, defined as the derivative of the sigmoid function T. (x))) The opaque density p(u) is computed based on the rate of change of the occupancy field 4, along the ray direction.
Signal representation We employ a separate MLP that takes the 3D spatial position x as input to predict the reflection coefficient a. The effective input amplitude A is treated learnable global parameter, since the transmitted amplitude is fixed and consistent across all experiments.
Discretization We adopt the same discretization scheme as in [55], using alpha-bles blending for opaque density and transmittance estimation. The surface normal n is computed as the gradient the SDF
Integration According to Eq. the signal received at each antenna is computed as an integration the amplitude defined in Eq. 5 To evaluate this integral, we apply Monte Carlo sampling by tracing Nay rays per antenna across space.
However, when combining all components, the overall computational complexity becomes pro- hibitively high. Assuming we sample N₂ depth points along each ray, and trace Nay rays for each antenna. The MLP network incurs computational cost of Ciii- The complexity of the signal tracing step for both forward and backpropagation is given by
(NNmy NannyNNNNannCCmmmm
(6)
In practice, to achieve 1mm resolution, the number of voxels becomes very large. If the synthetic aperture antenna array contains on the order of 10s antennas, the total computation quickly reaches unrealistic limits. Even with GPU acceleration, single forward pass in this setting has previously taken over one hour to compute. This scale far exceeds the number of available GPU threads and makes backpropagation based learning infeasible under this brute-force computation strategy.

---

O 4
Signal Tracing
Antennai
Aperture
Aperture
Patchel Apurturr
Traditional Sampling
Leuskes Sampling
Figure 5: Left: Naive sampling traces rays acToss the entire space for each antenna, resulting in the highest computational complexity. Middle: Lensless sampling strategy samples points along the radar's primary ray direction and reuses network predictions across antennas. Alpha blending and transmittance are calculated using lens-less alpha blending. Right: Signal Tracing Bank: subset of antennas processed in each iteration, reducing computation for both forward and backward passes.
6 Lensless Sampling & Lensless Alpha Blending
To make volumetric rendering computationally feasible. we observe that the previous Monte Carlo sampling strategy contains significant redundancy. Specifically, rays emitted from different antennas frequently intersect at the same spatial locations, leading to duplicated computations. A straightfor- ward solution might be to adopt uniform grid-based sampling in 3D space. However, this approach fails to preserve the structure of transmittance accumulation along individual rays, which is critical for accurate signal modeling.
To address both redundancy and physical correctness, we propose lensless sampling. The key insight share the computation of opaque density and accumulated transmittance across different antennas, where p(u) and 7 Tuu) only need to be computed once for each position, for all antenna rays that intersect the pose.
Primary Ray We first sample parallel rays aligned with the radar's 's primary direction wp. The starting points Xapr of these rays are sampled within the antenna aperture, and points are then sampled along each ray. Initially, we ignore the fact that p(u) and Twu) depend on the real ray direction, effectively replacing all individual receive directions w, with the shared primary direction wp. The sampled points are formulated as x = Xapr + up, making the assumption that these values are shared across all rays that pass through the same spatial location. Specifically, for each position, we compute p(u) and T(u) by using (55) once and reuse them for all antennas. We choose the radar's primary direction as the ray direction in order minimize the discrepancy between the approximate and the actual rays emitted from the antennas.
Lensless Alpha Blending Neglecting the dependencies of p(u) and T(u) the ray direction can, in principle, lead to inaccuracies. However, we prove in Appendix [. that when computing
a = exp f p(u) du) the influence of the ray direction cancels out, and a, remains identical to the value computed along the primary ray.
Furthermore, for the accumulated transmittance, can show that, starting from the logistic distribu- tion, the difference in T(u) across different ray directions is equal to the difference in sigmoid-SDF values at the ray origins. Details of the derivation are provided in Appendix B.2] Formally. the transmittance along a real ray direction T'u') can be efficiently derived from the transmittance along the primary ray direction T(u by adjusting with the sigmoid-SDF values the respective ray origins, rather than computing ray-dependent product of accumulated alpha values:
T(u') = T(u) - &. (x((x))))) + &. (((xx)))))
where (-) denotes the cumulative density function (CDF) of logistic distribution, and x(ux). x(u,) the starting points of the respective rays. The power decay term y, well the time and phase components, are computed deterministically using the actual rays. Additionally, the Sigmoid-Sp value at the ray origin, used to correct accumulated transmittance, is excluded from backpropagation, ensuring computational efficiency.
Signal Tracing Bank In Eq. E.2 we iterate over sampled points, and for each point, we perform signal tracing across antennas. Within each signal tracing operation, we again loop over all sampled points along the ray. Although these computations unavoidable, we can significantly
7

---

Line of Sight
Non-Line of Sight Knife
Bunny
Elephant
Bottle
Knife
Boat
Elephant
Object
Canera
Our Result Matched Filter
Figure 6: Left: Experimental results of objects with nothing between the radar and the object. Right: Results when the object is occluded from the radar. The camera scan done in line of sight.
reduce the computational burden by leveraging the fact that the matched filter contains no trainable parameters. This allows During training, we implement a memory bank mechanism. We divide the antenna aperture into patches, analogous convolutional processing. In each training iteration, signal tracing performed only for antennas within single patch, while the signal tracing results for antennas in the other patches are retrieved from the memory bank. After each iteration, the memory bank is updated with the newly computed results for the current patch.
signal tracing results of most antennas from previous iterations.
Computational complexity By reusing the primary rays, only need evaluate the shared opaque density and accumulated transmittance ON times for both the forward and backward passes. The lensless approximation affects only the forward computation, and with the introduction of the Signal Tracing Bank, the effective antenna size becomes constant per iteration. Thus, the overall For the deterministic components
learnable component complexity is reduced to O(NNy--N,Cmll of signal tracing and matched filtering, the computation time remains bounded by ONN for both forward and backward propagation. This results in a total complexity that is significantly lower than the original formulation given in Eq.
50
Fig, presents the latency and GPU memory usage across different sampling configurations.
Antenna
40
Ray Nay V.s. Latency
& Memory Usage (GB)
40
1024, N, 32, and a patch size of 3200 antennas for the Signal Tracing Bank. Without lensless sampling, training becomes infeasible due to the excessive memory demand of simultaneously processing Nam sample points. The Sig- nal Tracing Bank drastically reduces both train- ing time and memory consumption. We adopt this baseline configuration for all experiments to ensure efficiency.
The baseline is chosen with Nay
Depth N. v.s. Latency
Memory
Latency
Ray Nay vis. Memory
Depth Memory
10
1600 3200 6400
W.D. 52.2 1024 bank
2048 W.B. 32 64 128
lens-less
Figure 7: Solid lines indicate latency (time per iteration), while dashed lines show GPU memory usage. Green represents the configuration antenna size in the Signal Tracing Bank. Orange represents the ray number, and "w.o. lens-less" refers to disabling lens-less sampling, whose mem- ory usage is too large to measure experimentally and is instead deduced from theory. Purple repre- sents the number sample points on each ray.
Optimization
patch
Dynamic loss masking Simply minimizing the difference between the rendered radar image and the ground truth using an L. loss is not effective in radar imaging. This is because radar mea- surements are based on reflected signals, which are affected not only by the presence of volume density but also by the reflection direction. This means that weak signal response in the rendered radar image can be caused by two distinct factors:

---

(I) low volume density at the sampled location, or (2) high volume density with the signal being reflected away from the receiver due to geometric misalignment. This inherent ambiguity makes direct supervision unreliable.
To address this, introduce masking strategy during training. If sampled point has exhibited high matched filter response in any previous iteration but produces low power in the current iteration, we treat it as unreliable and exclude from the loss computation. This prevents penalizing the model for physically valid signal paths that fail to reflect back to the receiver.
8 Experiments
Dataset & Implementation As there are no publicly available radar datasets for near range imaging, collected our own dataset using TI 1843B0OST mmWave radar (53) with DCA 1000EMM for raw data collection, attached to a Franka Research 3 controlled via the FrankaPy library [64]. We emulate 2D arrays by moving the radar with the robotic arm for 17-68 array viewpoints per object. For ground truth collection we used the Scaniverse App with an iPhone, which uses camera and LiDAR scans to compute 3D meshes and pointclouds. We trained our model for 50,000 iterations over 32 hours on a single NVIDIA H100 GPU. See the Appendix for detailed data description and training hyperparameters.
Comparison to Baseline As baseline, we compare results from GeRaF to the matched filter summation of all images. Since the corresponding output is a heatmap, we threshold the image and perform Poisson surface reconstruction on the point cloud. Qualitative comparisons are shown in Fig. 6 The object imaged is shown in the first row, followed by the camera scan, then the baseline matched filter reconstruction, and finally the output from GeRaF. The first four columns show reconstruction results when the object is line of sight of the radar, and the last three columns show results for the object when it is occluded with a box or large sheet of paper. In the non-linee non-linc-of-sipee processing, the box is cropped out of the MF image to just reconstruct inside the box. It is clear that the extracted meshes from the matched filter ground truth are not as complete as the outputs from GeRaF, and our method works even when vision methods would fail (eg. when line of sight is completely blocked).
Table 1: Quantitative results ("oceluded).
F11
CD(mm)
Object
MF
Ours
MF
Ours
Wrench
0.52
0.88
0.29
0.07
Bunny
0.56
0.81
0.03
0.01
Elephant
0.65
0.79
0.25
0.14
Bottle
0.44
0.97
0.35
0.05
Knife
0.44
0.71
0.59
0.49
Star
0.80
0.86
0.60
0.50
Knife"
0.45
0.83
0.84
0.26
Elephant*
0.42
0.52
0.52
0.46
Boat"
0.51
0.69
0.35
0.30
data
Wrench 0.63
0.86
0.29
0.14
Though the output of GeRaF is perhaps, not detailed as the camera scans, this is primarily because our scans are limited to a single pitch axis, limiting the amount of reflec- tions we can receive back from the objects, this limitation is discussed in more detail in Appendix F Fig. 3 shows ex- amples of the similarity between the rendered output and the ground truth radar heatmap. More qualitative results will be included in supplementary material.
Quantitative results are shown in Tab. 1 for F1-Score (F1) and the Chamfer Distance (CD) millimeters, ex- plained in Appendix C where points were sampled uni- formly on the camera mesh the matched filter mesh and GeRaF's mesh. We see that method outperforms the Matched Filter (MF) baseline in both F-Score and Chamfer distance with a slight drop in accuracy when the items are moved into the box. More ablation studies are included in supplementary material.
Real
Rendered
Figure 8: Rendered heatmaps compared real heatmaps.
Novel View Synthesis We also demonstrate GeRaF's ca- pability in novel view synthesis (NVS) [39 To evaluate this, we split the available views into training and test sets. GeRaF trained using only the training views and evaluated on the held-out test views. To assess the quality of the synthesized matched filter images, we compute the Peak -i-nal-to-Noise

---

Ratio (PSNR) between the rendered outputs and the ground truth matched filter images. PSNR is widely used metric for evaluating image reconstruction fidelity, where higher values indicate closer similarity to the reference. The Peak Signal-to-Noise Ratio (PSNR) is computed as:
MAX² MSE
PSNR = 10 log10
(8)
where MAX is the maximum possible pixel value of the image, and MSE is the mean squared error between the synthesized image and the ground truth. Unlike RGB images, matched filter (MF) images not have a fixed maximum pixel value. To make PSNR computation meaningful, we normalize each MF image by dividing by its maximum value. As result, the maximum possible pixel intensity set to M AX 1.0 for all PSNR evaluations.
We present four novel view synthesis results on the Bunny object in Fig. The selected views include the front, left, back, and right sides of the object. each case, GeRaF produces visualizations that closely match the ground truth. Quantitatively, the synthesized matched filter images achieve PSNR values around dB, demonstrating the feasibility and effectiveness of GeRaF for novel view synthesis tasks.
Target Image
Top View
Side View
Front View
Top View
Side View
Front View
Synthesis Imuge
Top View
Side View
Front View
Top View
Side View
Front View
Novel Plane PSNR=30.5dB
Novel Plane 2, PSNR-27.4dB
Target Image
Top
Top
Synthesi Image
Top View
Side View
Front View
Side View
View
Novel Plane 3, PSNR-29.2dR
Novel Plane 4, PSNR-27.9dB
Figure 9: Novel view synthesis on four selected novel planes. We visualize the 3D matched filter images by projecting them along three axes using maximum intensity projection.
Conclusion
In this paper, we proposed the first method for applying differentiable rendering radio frequency in the context of high resolution geometry reconstruction. Our method addresses the fundamental differ- ences between RF and optical imaging, and we show evaluation on real captured data. We proposed physics-awan volumetric rendering pipeline that incorporates radar-specific signal propagation and reflection models and a novel lensless sampling strategy to significantly reduce computational cost while maintaining resolution. Though there are limitations, discussed in Appendix F this paper serves as the first step towards making millimeter-lewe geometry reconstruction feasible with neural representation learning.
we believe that
10

---

Acknowledgments
We thank the anonymous reviewers and members of the SENS Lab for their valuable feedback. We would also like to thank Ralf Boehnke, Dymtro Rachkov and Daniel Ardila Palomino from Sony Research for their helpful feedback. This project is funded in part by the Sony Faculty Innovation Fellowship.
References
[1] Fadel Adib, Chen- Yu Hsu, Hongzi Mao, Dina Katabi, and Frédo Durand. Capturing the human figure through wall. ACM Transactions Graphics (TOG), 2015.
[2] Fadel Adib and Dina Katabi. See through walls with wifi! In SIGCOMM, 2013.
[3] Fredrik Andersson, Randolph Moses, and Frank Natterer. Fast fourier methods for synthetic aperture radar imaging. IEEE Transactions on Aerospace and Electronic Systems, 2012.
[4] Emile Barbier-Renard, Florence Tupin, Nicolas Trouvé, and Loie Denis. Multi-view 3d surface reconstruction from images by inverse rendering. 2025.
[5] Jacob Benesty, Jingdong Chen, and Yiteng Huang. Conventional beamforming techniques. Microphone array signal processing, 2008
[6] David Borts, Erich Liang, Tim Broedermann, Andrea Ramazzina, Stefanie Walz, Edoardo Palladin, Jipeng Sun, David Brueggemann, Christos Sakaridis, Luc Van Gool, al. Radar fields: Frequency-space neural scene representations for finew radar. In SIGGRAPH, 2024.
[7] Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Barron, Ce Liu, and Hendrik Lensch. Nerd: Neural reflectance decomposition from image collections. In ICCV, 2021.
[8] Hanlin Chen, Chen Li, and Gim Hee Lee. Neusg: Neural implicit surface reconstruction with
3d gaussian splatting guidance. arXiv preprint, 2023.
[9] Xingyu Chen, Zihao Feng, Kun Qian, and Xinyu Zhang. Radio frequency ray tracing with neural object representation. arXiv preprint, 2024.
[10] MMDetection3D Contributors. MMDetection33:: OpenMMLaab next-generation platform for general 3D object detection. https://githuut com
httppp///g.tttbo...om///meaaaa/a hataaa/mmmmmmmmmmmeetetiiii... 2020.
[11] Robert L Cook and Kenneth E. Torrance. A reflectance model for computer graphics. ACM Transactions on Graphics (TOG), 1982.
[12] Laura Dodds, Tara Boroushaki, Cusuh Ham, and Fadel Adib. Mito: A millimeter-wave dataset and simulator for non-line-of-sipe perception. arXiv preprint, 2025.
[13] Laura Dodds, Hailan Shanbhag, Junfeng Guan, Saurabh Gupta, and Haitham Hassanieh. Around the corner mmwave imaging in practical environments. In Proceedings of the 30th Annual International Conference Mobile Computing and Networking, pages 953 967,967,-1 2024.
[14] Zhao Dong, Ka Chen, Zhaoyang Lv, Hong-Xing Yu, Yunzhi Zhang, Cheng Zhang, Yufeng Zhu, Stephen Tian, Zhengqin Li, Geordie Moffatt, et al. Digital twin catalog: A large-scale photorealistic 3d object digital twin dataset. In CVPR, 2025.
[15] Herbert Edelsbrunner, David Kirkpatrick, and Raimund Seidel. On the shape of of points in the plane. IEEE Transactions on information theory, 1983.
[16] Yasutaka Furukawa, Carlos Hernández, et al. Multi-view stereo: A tutorial. Foundations and trends in Computer Graphics and Vision, 2015.
[17] Yasutaka Furukawa and Jean Ponce. Accurate, dense, and robust multiview stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2009.
[18] Jian Gao, Chun Gu, Youtian Lin, Zhihao Li, Hao Zhu, Xun Cao, Li Zhang, and Yao Yao. Relightable 3d gaussians: Realistic point cloud relighting with brdf decomposition and ray tracing. In ECCV, 2024.

---

[19] Chun Gu, Xiaofei Wei, Zixuan Zeng, Yuxuan Yao. and Li Zhang. Irgs: Inter-reflective gaussian splatting with 2d gaussian ray tracing. In CVPR, 2025.
[20] Junfeng Guan, Sohrab Madani, Suraj Jog, Saurabh Gupta, and Haitham Hassanieh. Through fog high-resolution imaging using millimeter wave radar. In CVPR, 2020.
[21] Junfu Guo, Yu Xin, Gaoyi Liu, Kai Xu, Ligang Liu, and Ruizhen Hu. Articulatedgs: Self- supervised digital twin modeling of artieulated objects using 3d gaussian splatting. arXiv preprint, 2025.
[22] Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Rares Andrei Ambrus, Jeannette Bohg. Abhinay Valada, and Thomas Kollar. Carto: Category and joint agnostic reconstruction of articulated objects. In CVPR, 2023.
[23] Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In SIGGRAPH,, 2024.
[24] Tianshu Huang, John Miller, Akarsh Prabhakara, Tao Jin, Tarana Laroia, Zico Kolter, and Anthony Rowe. Dart: Implicit doppler tomography for radar novel view synthesis. In CVPR, 2024.
[25] Yingwenqi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xiaoxiao Long, Wenping Wang, and Yuexin Ma. Gaussianshader: 3d gaussian splatting with shading functions for reflective surfaces. In CVPR, 2024.
[26] Haian Jin, Isabella Liu, Peijia Xu, Xiaoshuai Zhang, Songfang Han, Sai Bi, Xiaowei Zhou, Zexiang Xu, and Hao Su. Tensoir: Tensorial inverse rendering. In CVPR, 2023.
[27] Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing. 2006.
[28] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics (TOG), 2023.
[29] Justin Kerr, Chung Min Kim, Mingxuan Wu, Brent Yi, Qianqian Wang, Ken Goldberg, and Angjoo Kanazawa, Robot see robot do: Imitating articulated object manipulation with monocu- lar 4d reconstruction. In Conference on Robot Learning (CoRL), 2024.
[30] Belal Korany, Chitra R Karanam, Hong Cai, and Yasamin Mostofi. Xmodal-id: Using wifi for through-wall person identification from candidate video footage. In MobiCom, 2019.
[31] Haowen Lai, Gaoxiang Luo, Yifei Liu, and Mingmin Zhao. Enabling visual recognition at radio frequency. In MobiCom, 2024.
[32] Ren Li, Cong Cao, Corentin Dumery, Yingxuan You, Hao Li, and Pascal Fua. Single view garment reconstruction using diffusion mapping via pattern coordinates. In SIGGRAPH, 2025.
[33] Zhihao Liang, Qi Zhang, Ying Feng, Ying Shan, and Kui Jia. Gs-ir: 3d gaussian splatting for inverse rendering. In CVPR, 2024.
[34] Yang Liu, Feng Wang, Naiyan Wang, and Zhaoxiang Zhang, Echoes beyond points: Unleashing the power of raw radar data in multi-modality fusion. In NeurIPS, 2023.
[35] Yuan Liu, Peng Wang, Cheng Lin, Xiaoxiao Long, Jiepeng Wang, Lingjie Liu, Taku Komura, and Wenping Wang. Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images. ACM Transactions on Graphics (TOG), 2023.
[36] Haofan Lu, Christopher Vattheuer, Baharan Mirzasoleiman, and Omid Abari. Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction. In ICML, 2024.
[37] Sohrab Madani, Jayden Guan, Waleed Ahmed, Saurabh Gupta, and Haitham Hassanieh. Rada- tron: Accurate detection using multi-resolltion cascaded mimo radar. In ECCV, 2022.
12

---

[38] Rakesh Raj Madavan, Akshat Kaimal, Vinayak Gupta, Rohit Choudhary, Chandrakala Shammu- ganathan, Kaushik Mitra, et al. Ganesh: Generalizable nerf for lensless imaging. In WACV, 2025.
[39] Ben Mildenhall, Pratul Srinivasan, Matthew Tancik, Jonathan Barron, Ravi Ramamoor- thi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 2021.
[40] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphies (TOG), 2022.
[41] Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müllrr, and Sanja Fidler. Extracting triangular 3d models, materials, and lighting from images. In CVPR, 2022.
[42] Michael Niemeyer, Lars Mescheder, Michael Occhsle, and Andreas Geiger. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In CVPR, 2020.
[43] Ming-Hao Ren, Xi Liao, Jihua Zhou, Yang Wang. Yu Shao, Shasha Liao, and Jie Zhang, Diffuse scattering directive model parameterization method for construction materials at mmwave frequencies. International Journal of Antennas and Propagation, 2020.
[44] Mark A Richards, Jim Scheer, William Holm, and William L Melvin. Principles of modern radar. 2010.
[45] David M Sheen, Douglas L. McMakin, and Thomas E Hall. Three-dimensional millimeter- wave imaging for concealed weapon detection. IEEE Transactions on microwave theory and techniques, 2001.
[46] Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron. Nerv: Neural reflectance and visibility fields for relighting and view synthesis. In CVPR, 2021.
[47] Yue Sun, Zhuoming Huang, Honggang Zhang, Zhi Cao, and Deqiang Xu. 3drimr: 3d reconstruc- tion and imaging via mmwave radar based on deep learning. In IEEE International Performance, Computing, and Communications Conference (IPCCC), 2021.
[48] Yue Sun, Zhuoming Huang, Honggang Zhang, and Xiaohui Liang. 3d reconstruction of multiple objects by mmwave radar on uav. In 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), 2022.
[49] Yue Sun, Honggang Zhang, Zhuoming Huang, and Benyuan Liu. R2p: A deep learning model from mmwave radar point cloud. In International Conference on Artificial Neural Networks, 2022.
[50] Harshvardhan Takawale and Nirupam Roy. Spinr: Neural volumetric reconstruction for finew radars. arXiv preprint, 2025.
[51] Towaki Takikawa, Joey Litalien. Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. Neural geometric level of detail: Real-time rendering with implicit 3d shapes. CVPR, 2021.
[52] Weiyi Tan, Yu Wang, Biao Tian, and Shiyou Xu. Fast 3d reconstruction of space targets from isar image sequences based on neural network. In IEEE 8th International Conference on Vision, Image and Signal Processing (ICVISP), 2024.
[53] TI Inc. Texas instrument awrl 843. https: http://wwww.tt.oopoeeetettt http://ww...oo/prrccttt/taaaaaa 2023.
[54] Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T Barron, and Pratul P Srinivasan. Ref-nerf: Structured view-dependent appearance for neural radiance fields. In CVPR, 2022.
13

---

[55] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurlPS, 2021.
[56] Ting Wu, Theodore S Rappaport, and Christopher M Collins. Safe for generations to come: Considerations of safety for millimeter waves in wireless communications. IEEE microwave magazine, 2015.
[57] Muhammet Emin Yanik and Murat Torlak. Near-field 2-d sar imaging by millimeter-want radar for concealed item detection. In 2019 JEEE radio and Wireless Symposium (RWS), 2019.
[58] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. In ECCV, 2018.
[59] Yao Yao, Jingyang Zhang. Jingho Liu, Yihang Qu, Tian Fang, David McKinnon, Yanghai Tsin, and Long Quan. Neilf: Neural incident light field for physically-bee material estimation. In ECCV, 2022.
[60] Yuxuan Yao, Zixuan Zeng, Chun Gu, Xiatian Zhu, and Li Zhang. Reflective gaussian splatting. In ICLR, 2025.
[61] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurlPS, 2021.
[62] Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. Multiview neural surface reconstruction by disentangling geometry and appearance. In NeurlPS, 2020.
[63] Zehao Yu and Shenghua Gao. Fast-mvsnet: Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement. CVPR, 2020.
[64] Kevin Zhang, Mohit Sharma, Jacky Liang, and Oliver Kroemer. A modular robotic arm control stack for research: Franka-interface and frankapy. arXiv preprint arXhv:2011.022340 2020.
[65] Mingmin Zhao, Tianhong Li, Mohammad Abu Alsheikh, Yonglong Tian, Hang Zhao, Antonio Torralba, and Dina Katabi. Through-wall human pose estimation using radio signals. In CVPR, 2018.
[66] Mingmin Zhao, Yingcheng Liu, Aniruddh Raghu, Tianhong Li, Hang Zhao, Antonio Torralba, and Dina Katabi. Through-war wall human mesh recovery using radio signals. In ICCV, 2019.
[67] Mingmin Zhao, Yonglong Tian, Hang Zhao, Mohammad Abu Alsheikh, Tianhong Li, Rumen Hristov, Zachary Kabelac, Dina Katabi, and Antonio Torralba. Rí-based 3d skeletons. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication, 2018.
[68] Xiaopeng Zhao, Zhenlin An, Qingrui Pan, and Lei Yang. Nerf2: Neural radio-frequency radiance fields. In MobiCom, 2023.
14

---

Appendix
This appendix organized as follows:
Section
provides more extensive radar background information.
Section
provides the proof for our lenless alpha blending.
Section
describes in detail the experimental parameters for collecting data.
Section
describes the setup for the Novel View Synthesis experiment,
Section
presents ablation studies,
Section
goes into detail on limitations, future work and societal impact.
A Radar Background
A.1 Free-space Power Decay
In free space, the power radio frequency signal attenuates proportionally to the square of the distance it travels due spherical spreading. When considering round-trip path, where the signal travels from the transmitter to point in space and then reflects back to the receiver, the decay is pronounced. This is known round-rrip free-space path loss, and the power decay factor reflected from distance u is given by:
Pa
(4Tu)* Pox
This expression accounts for two instances of inverse-square spreading: one during transmission to the point and another during reflection back to the receiver. As such, the received power decreases proportionally to 1 I/u".
A.2 Radar Reflections
Building off of Sec. we primarily expect specular reflections, albeit some flexibility in the signals which return to the receiver. Even though perfectly specular signal will only return the receiver if the reflecting surface is normal to the transmitted chirp, in actuality, there is margin in which the signal is not perfectly specular. yet some signal still returns. Similar commons ways of modeling diffused reflections, we follow shifted Lambertian model
[4] 43, sometimes also called the directive model. We represent the incident direction by the unit vector wi, the surface normal by n, and the ideal specular reflection direction by:
W, = wi 2n w;)n
(10)
The ray which returns to the receiver antenna is denoted wr, and is calculated by taking the unit vector that goes from the surface point to the receiver location. The received power is modulated by the directional scaling factor:
(Wo wr)
(11)
following the scaled Lambertian model men- tioned above.
A.3 Near Field vs. Far Field
Wireless imaging systems operate differently depending on the distance between the sensor and the target. This is denoted as near-field (or near range) and far-field (or far range) imaging. When an object is in the far-field, this is catego- rized as when the distance between the object and the radar are much larger than the wave- length and the aperture of the antenna system. For example, 10's of meters away. In this case,
Reflector is far away
Reflector is nearby
Figure 10: Difference between far-field and near- field rays. On the left, it's shown that the incoming rays can be approximated nearly parallel, while on the right, when the object is close to the anten- nas, can longer make this assumption.
15

---

the incoming waves can be approximated as planar 44] parallel to each other. This significantly simplifies the image reconstruction, assuming the antennas are uniformly spaced, because (1) the corresponding filter weights can be reused across depth and (2) the weights are uniform, allowing us use beamforming and Fourier Transforms speed up computation time [3. However, operating in the far-field comes at the cost of lower resolution reconstruction, since the RF waves spread out the further travels.
Meaning all the incoming signals are
On the other hand, in this paper, we are operating in the near-field. This mean the object is much closer to the antenna aperture, within a meter in our case, Im- portantly, this means that the incoming waves that are reflected from the object in the scene can no longer be approximated as parallel. The difference in sig- nal paths is illustrated in Fig. TO In this case, it is required that the exact signal propagation paths are taken into account, otherwise the radar images ap- pear distorted [57 [5]. To correctly reconstruct an undistorted radar image, need to use matched filter (see Sec. 3). which comes with a high compu- tational complexity, albeit higher resolution image. For example, in Fig. [TT a heatmap of a wrench shown when processed with beamforming using the matched filter.
Matched Filter
Beamform
Figure 11: Difference between processing radar data captured in -ield with matched filter vs beamforming.
A.4 Differentiable Matched Filter & Signal Tracing
We have the forward path of the Matched Filter shown in Eq. E.2
N
P(x,)
Σ
si,,t)
gR @aatt xj Upis
The backpropagated gradient to the signal s(i, t) is given by:
0L
aL P(x)
Σ
ff2kktt -2x
(12)
ds(i,t)
ap(x)
xj En
Signal Tracing is given by Eq
s(i,t) = Σ
Am(x)) e -Rakksz -22
XyEβm
The backpropagated gradient to the amplitude A (x,) given by:
8L
0L
Bankt j2afs
(13)
0Ax(xx)
ds(i,t)
i=1
A.5 Code Implementation
Differentiable Matched Filter We implement both the forward and backpropagation of the matched filter in CUDA using parallel computation. The corresponding pseudocode is shown in Alg. [
16

---

Algorithm Differentiable Matched Filter Algorithm
[1] Forward: Input signal s(i, L) where = O,..., Nant 1, = ..... N₂. 1;
sampling points x, where = O..... Nay N₂
for Parallel j 0...., Nay N₂ do
P(x;) 5- 0
for i O,..., Nam 1 do
for t = ......,............... N₁
do
P(X;) <- P(x;) s(i,t) eoakra elaafT
P(x;) + [
Output: P(x;) for j
[2] Backward: Gradient of matched filter power 8P(x, for j = D,..., Nay N,
0L
Input signal (i, t) where 0, ..... ant 1, 0, N₁ 1;
Sampling points xj where j = 0...., Nuy N₂
for Parallel 0,..., Nan do
for Parallel
0. ......,,, N₂ do
8L <
[s(1.1]
for j = 0, O,..., Nay N₂ do 9L
8L (s(C.) P(x)) 8P(x))
e j2nkdt e- j25f=
(a(f,r)
Output: 8s(k.)
0L for all i,t
Signal Tracing We implement both the forward and backpropagation in CUDA using parallel computation. The corresponding pseudocode shown in Alg. 2
Algorithm 2 Signal Tracing
[1] Forward:
Sampling points x, where = 0,.... My N₂ 1; Amplitude Arx (x,) where j = N.a..NNN,.NN
for Parallel .,..., Nant do
for Parallel = 0, ......,,,, N. do
sii,t)
for j = O...., Nay - do
s(t, + s(i, t), + Ax) e p2akkt --2af
Output: (i, for all i,
[2] Backward: Gradient of signal AL for = O,..., Nani - 1, t- = -...., No -1
8a(1.t)
Sampling points
X, where j = 0, ..... Nay Nay.N N₂
for Parallel j O...., Nay.N, do
+ 0
JA.
for i 0.... N
do
am
for = 0. O....,N. N₁ do
0L t (An(x,) + al 0Ar(x,)
Ac
da(1.))
0L for all j Output: (x,]
B Lensless Alpha Blending
B.1 Alpha
Following [55]. the opaque density computed along the primary ray p is defined as:
d#, du
(f((()))
p(u)
(14)
+ (f((())))
where ( and (x) denote the probability density function (PDF) and cumulative distribution function (CDF) of the logistic distribution, respectively.
17

---

The corresponding alpha value over the interval [ug, u++1] is then:
a; = 1 - exe (-Sma
Me+1 plu))du)
d0 (f((()))
= - exp
-xep
4 ((((())))
du
(15)
When changing from the primary ray p to real ray r, the derivative term in the numerator transforms as:
dΦ du
d&, du' (f(x(u))) du p'('')
du'
du'
(f(x(u)))
(16)
du
where u and u' are the sampling parameters along the primary and real rays, respectively, and p'(u') denotes the opaque density computed along the real ray.
In integration, we have:
(-
-exp (- Mi
du
)
= exp
p(u) (u = exp
p(u) du'
(17)
du
By substituting the transformed opaque density from Eq. T6 into
Eq.
we obtain:
1 - exp (-Jx/t
wi+ ('(')) du' a',
(18)
demonstrating that the computed a, remains unchanged when transitioning from the primary ray to the real ray.
B.2 Transmittance
Following
[55] the primary ray direction p. we define the transmittance as:
d$ T(u)p(u) = [x((())) pl (x(((x)))) = = du (f(x(u))),
(19)
where (-) and $ (.(.) are the PDF and CDF of the logistic distribution, respectively.
Given the transmittance is defined ((u) exp (-Jo p(u) du). differentiating both sides yields:
dT d
(("((()d -p(u) exp (-fr- p(o)do) =-((u)T(u),
exp
du du
which implies:
dT T(u)p(u) du
(u).
(20)
Combining Eq.
19 and Eq. 20 obtain:
d
(f(((x))) = dT du
u
(21)
du
Because the geometry-dependee signed distance function (SDF) does not vary with ray direction, we assume the boundary condition:
T o.(f((())) (f((())))))
Then, given the starting CDF values & (x((x))) on the primary ray p and 4. (f(((())) on the
real ray r, integrating Eq. 21 leads to:
T(w) - T(u) = 4 (f((()))) 4 (f(((x))))),
(22)
which completes the proof of Eq. 7
18

---

Wrench
Bottle
Elephant
Front View
Side View
Top View
Figure 12: Subset of matched filter images used as ground truth in GeRaF. The first row shows the radar heatmaps summed across the depth, giving front view of the object relative to GeRaF's coordinate system, the second row is summed across the horizontal axis (azimuth), giving a side view and the last row is summed along the vertical axis (elevation), giving top down or bird's ' eye view.
C Experiment Details
Data Collection Setup We collected our own dataset using a TI 1843B0OST mm Wave radar (53 with a DCA 1000EVM for raw data collection, attached to Franka Research 3 controlled via the
FrankaPy library 644. We emulate 2D arrays by moving the radar with the robotic arm for 17-68 (more scans were made for more complex objects) different array viewpoints, which are each 0.1 0.14m
x 0.25m in size with an antenna spacing of ~ ( - between all antennas. Each imaging plan was shifted circularly (yaw) around the center of the objects location to measure various viewpoints. A subset of the ground truth heatmaps are visualized in Fig. [2] Though the actual groundtruth used in GeRaF a 3D heatmap, for ease visualization, we plot the 3D heatmap summed along each the 3 dimensions (depth, azimuth, elevation), respectively. Each of the columns show the radar heatmap from a different scanning plane.
In order to capture 360 scans, we use rotation platform to rotate the object which was placed aroun 0.3-0.5m away. The radar parameters were have a starting frequency of 77 GHz usable sweeping bandwidth of 3.59 GHz, giving 0.04m in range resolution. The slope used is 70.1 (MHH5( capturing 64 ADC samples with 1250ksps sampling rate for each chirp.
Training Setup For the SDF Network, we use an MLP with 8 layers and a hidden dimension of 256. We apply sinusoidal positional encoding with 10 frequency levels as input. The Reflectivity Network is implemented as an MLP with 4 layers and a hidden dimension of 256. The signal power prediction implemented single trainable parameter.
We trained our model for 50,000 iterations over 32 hours on a single NVIDIA H100 GPU, using DnDetection3D [10] as the code base. We used an initial learning rate of x 10 10-3 3 but due to the sparsity of the input, the learning rate for the SDF Network was reduced
x 10-4 Training was performed using the Adam optimizer with cosine annealing learning rate scheduling.
Metric Calculation The metrics used to compare GeRaF and the matched filter baseline against the camera ground truth are the F1-Score (evaluated with r = 0.01) and Chamfer distance. These two metrics evaluate the similarity between two 3D point clouds. For mesh to point cloud conversion, we sample uniformly along the meshes for each of the three candidates with 5000 points. We then align the matched filter point clouds the camera baseline and align GeRaF's point clouds the camera baseline.
The F1-Score is calculated by finding the nearest point in the camera point cloud and computing the distance in both directions. Precision and recall are computed as the proportions of these distances that fall below 7, and the F1-Score is derived by:
F1 2 Precision Recall
(23)
Precision + Recall
19

---

The Chamfer distance computes the pairwise distance between two pointclouds by finding the nearest neighbors for each point between point clouds and calculating the squared distance, returning the average distance. This is calculated in both directions.
D Novel View Synthesis
We also demonstrate GeRaF's capability in novel view synthesis (NVS) [9] To evaluate this, we split the avail- able views into training and test sets. Figure [3 illustrttess the detailed data split. The results are shown in Fig. 9
nevll Plana
Novel Plane
Navoel Plane
E
Ablation Studies
E.1
Number of Radar Scanning Planes
We study the effect of varying the number of radar scan- ning planes (analogous the number of views in vision- based reconstruction) used during training. A total of 104 planes were measured cover a full 360-degree view of the object. We uniformly sampled subsets of these planes simulate different input settings.
Figure 13: Novel view synthesis setting. The blue planes represent novel views that are not included during training.
Even with only 8 planes, GeRaF is able to reconstruct
the basic shape of the Bunny, though the result contains
noticeable noise and inaccurate details. When the number of planes fewer than 40, the reconstructed shape becomes visibly distorted, and the noise level increases significantly. With more than 72 planes, the overall shape is correctly reconstructed, and differences are mostly limited to fine details. This indicates that GeRaF is robust the number of input planes once sufficient coverage threshold is met.
Overall, the results show clear trend: increasing the number of radar scanning planes improves the reconstruction quality, yielding more accurate geometry and less noise.
E.2 Number of Temporal Sample
We study the effect of the number of temporal samples N, on geometry reconstruction quality. According to the matched filter equation:
Nytoms
Nyues
Niw N
CC
P(x) =
Σ
tii dankni j2rfT
increasing the number temporal samples im- proves resolution. However, the computational cost of the matched filter increases with N,.
EL T- 0.72 [CD 0,18
Fi 081 [
[CD
To assess this trade-off, we evaluate reconstruc- tion quality under different values of No. shown in Fig. [5] Empirically, we observe significant degradation in performance as N₂ varies, demonstrating GeRaF's robustness to noise. While using fewer temporal samples can theoretically reduce the quality of the ground truth matched filter image, the increase latency with larger N, remains relatively limited. Consider- ing the balance between performance and efficiency, set Ne 64 in our final implementation.
Figure 14: Ablation study on the number of radar scanning planes. F1 denotes the F1-Score and CD denotes the Chamfer Distance. t indicates higher values are better, while J. indicates lower values are better.
20

---

N; 32
N: = 64
N: 128
Lat 3.66s|MEM 8.4GB
Lat 4.06s|MEM 8.4GB
Lat 4.54s|MEM 8.4GB
Nevel 6
Nevel 8
Nievel 10
Lat 3.57 8.4GB
Lat 3.84s|MEM 8.4GB
Lat = 4.06s|MER = 8.4GB
Figure 15: Top: Ablation study the number of temporal samples N₁. Bottom: Ablation study
the positional encoding frequency level Nieve)- Lat denotes latency, and MEM denotes GPU memory usage.
E.3 Positional Encoding Frequency Level
We apply sinusoidal positional encoding as NeRF 39 where each input x is mapped to higher-dimenssonall space using:
7xx) |sin πх), cos(22 RT), sin(2 Nevt Tι),, cose Moe-l Tx))
with Niveel controlling the number of frequency bands.
Low frequency levels (e.g., 20, 2 capture coarse, smooth variations, while high frequency levels (e.g.. 29, "2) cap- ture fine, high-frequency details such as sharp edges or thin structures. The number of frequency levels Nievel trols the trade-off between expressiveness and overfitting: higher Neeel enables modeling of finer details, whereas a lower Nievel reduces model complexity and helps prevent overfitting to noise.
Nyay 1024
May 2048
To assess this trade-off, we evaluate reconstruction quality under different positional encoding frequency levels Neevll as shown in the bottom Fig. 15
3.66s|MEM B4GB
14.6:|MMM 37.60R
We observe that higher frequency levels enable better re- construction of fine details in the Bunny and improve ro- bustness to noise in radio frequency signals. Additionally, increasing Nivel leads higher computational latency, but the increase is relatively limited. Considering this trade-off, we set Nievel 10 our final configuration.
N, 16
N₂ 32
1at 2.37s/MEM 4.3CR
Lat 3.66: MMMM RAGR
Figure 16: Top: Ablation study on the number of ray samples Naw- Bottom: Ablation study on the number of depth samples N.. Lar denotes latency, and MEM denotes GPU memory usage.
E.4 Resolution
In Section 6 of the main submission, we discuss the com- putational resources required by different training-time sampling resolutions.
21

---

For the number of sampling rays, results show that increasing the number of rays leads to higher memory usage and longer latency, but surprisingly worse reconstruction performance. possible explanation is that in radio frequency data, denser sampling introduces more noise, which slows convergence. As a result, under fixed number of training iterations, higher sampling resolutions may lead to suboptimal solutions due to insufficient convergence. Based on these observations, we set May = 1024 in our final configuration.
For the number of depth samples N₂. results indicate that fewer depth samples lead to lower reconstruction detail and a coarser overall shape. Although reducing depth samples improves training efficiency linearly in time, it comes at the cost of reduced fidelity. Therefore, we set N₂ 32 as our final configuration balance quality and performance.
E.5 Dynamic loss masking
In Section the main submission, we discuss the mo- tivation for using dynamic loss masking. The primary reason the inherent ambiguity in interpreting low-power regions in matched filter (MF) imaging. Specifically, weak signal response in the rendered radar image may arise from distinct causes: (I) low volume density at the sampled location, or (2) high volume density with the signal being reflected away from the receiver due geometric misalignment. This ambiguity makes direct supervision unreliable in low-reflection regions.
M.s. Masking
Masking Fi 0.01 [CD 1= 0.11
Ft 0.63 cb Im 0.32
Figure 17: Ablation study dynamic loss masking. "w.o." denotes without masking, and "w." denotes with mask- ing.
As shown in the top of Fig. 17 training without dynamic loss masking leads incorrect reconstruction. The model tends to interpret low-intensity regions as low density, resulting in artifacts-sus as distorted ear in the Bunny example-whed the true geometry reflects weakly due to its orientation rather than material absence. This highlights the importance of masking unreliable regions during training improve geometric fidelity.
E.6 Effect of Radar physics
We introduce radar physies in Section 3 of the main submission. Radar reflections are dominated by specular effects and are commonly approximated using the Lambertian model 44 In this section, we study the effect of incorporating the Lambertian model into our formulation.
To isolate its impact, remove the w. w. term from Eq. in the main submission. As shown the Fig. [8] the visual difference between using and not using the Lambertian model appears minor. However, quantitative results indicate that omitting the Lambertian term leads worse performance. Specifically, in the left-side view, the reconstructed bottom of the Bunny appears significantly larger than in the right-side view, contributing to higher Chamfer Distance error.
That said, the Lambertian model does not fully capture the physical behavior radar reflections, especially under specular and material neterial-dependent conditions. Thus, while it provides useful approximation, the Lambertian model is not an optimal representation of true radar physics.
F
Discussion
F.1
Potential Societal Impacts
There are a few potential negative impacts that RF neural representations could cause. Being able to perform 3D reconstruction of things behind occlusions could potentially raise concerns for privacy. Additionally like many other learning based work, the heavy compute power raises concerns over the environment sustainability. However, this work could also potentially help with things like suspicious package detection.
22

---

w.o. radar physies
radar physies
1= 0.72 CD 1= 0.19
t= 0.81 CD 0.01
w.o. radar physies
w. radar physics
F1 1- 0.70 CD 1- 0.21
F1 1- 0.79 | CD 1- 0.14
w.o. radur physics
w. radar physics f= 0.97 CD 1= 0.05
F1 1= 0.55 CD 1 0.35
Figure 18: Ablation study radar physics. "Without radar physics" indicates that the Lambertian is not used.
model 44
F.2
Limitations & Future Work
Radar Scans Our method currently is limited by the diversity of the networks radar heatmap ground truths collected. More specifically, if there was a point that existed on the surface of the object, which never reflects back to the radar in any of the scans taken, then the network has way of creating a point in that location, because it has never received any reflection from that location, making it invisible, Future work is required for dealing with unseen surfaces, and more comprehensive scanning methods.
Materials We currently tested the system with objects made of metals, which allows for more clear reflections. On the other hand, metal objects are very specular, meaning more radar scans are required to receive signals from all parts of the object. Dealing with objects made of other materials composition of materials left for future work, which will require significantly adjusting the radar reflection model.
Thick Occlusions We have shown results for some occlusions (cardboard, paper), however, in the case of thicker occlusions this problem is not straightforward since this requires the network to predict two or more surfaces rather than ignoring the weak occlusion reflections. Future work is required to extend the surface reconstruction to deal with multiple surfaces.
Better Radio Frequency Model As discussed in the radar physics section, unlike vision-based inverse rendering which often adopts the Cook-Torrane BRDF [I], with components such as the Fresnel term, Schlick-GGX geometry function, and Trowbridge-Rein GGX distribution to model diffraction and metallic reflections, the radio frequency (RF) domain still relies on the simplified Lambertian model. This model not even sufficient for accurate forward rendering, leading to significant reflection errors. As result, inverse rendering based on such coarse approximation
23

---

cannot reliably reconstruct detailed geometry, since the underlying reflection model introduces substantial errors.
Lower Computational Complexity Even with the introduction of lens-less sampling and alpha blending, which make real-time differentiable rendering feasible, the computational cost remains significantly higher compared to rision-based rendering. In the vision domain, reflective models benefit from techniques such as split-sum approximation [41] and spherical harmonies [54]. which avoid costly Monte Carlo sampling by operating efficiently over ID rays.
However, directly applying these techniques to radar reflection is not feasible, since traditional alpha blending operates along 1D rays, whereas radar rendering requires sampling over a 3D space. This dimensional difference introduces substantial complexity. Therefore, future work should explore how to achieve 3D space sampling with computational complexity comparable to that of ID ray-based sampling, in order to further improve the efficiency of RF-based differentiable rendering.
Accurate multi-level surface representation In dision-based geometry reconstruction, multi-level surfaces are typically not concern, surfaces occluded by others are not visible in RGB images and therefore do not contribute to reconstruction. However, this is not the case in radar sensing, where signals can penetrate and reflect off multiple layers of geometry, resulting in observable multi-level surfaces. As a result, representing and reconstructing such multi-layered structures becomes a key challenge in RF-based 3D reconstruction. Developing effective multi-level surface representation thus essential for fully leveraging the unique capabilities of radar.
24

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Our contribution claims to address the problem of re-formulating neural
geometry reconstruction for radio frequency, which is explains in detail in Sec. 5 and results
shown in Fig. 6
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: Yes, discussed in Appendix,
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model sell-specfiiaatinn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-tex, system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
25

---

Answer:
[NA]
Justification:
We do not have any theoretical results, only real-world experiments.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proof's can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided not)?
Question:
Answer: [Yes]
Justification: Our methodology (sampling and rendering) is detailed in Sec. 6 and full details the network setup and experiment set up are included in the Appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, descríbing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is new model (e-g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path reproducing verifying the results. 5. Open access to data and code
26

---

Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [No]
Justification:
We plan release the training code, experiment code and dataset in the future.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nnis.co cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https
/ ccpppi.ccppulli///ggg. guwdes/ddeeeee CooeeSumeeieeeeeeeeeee ssi onyolicy) for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data,
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: These details are discussed in the main paper and in more details in the Appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [No]
Justification: The paper addresses an optimization problem where the evaluation focuses on geometry reconstruction. As such, statistical significance measures are not applicable or necessary this context.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emor bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
27

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that out of range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: Yes the details are in Sec. g
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer:
[Yes]
Justification: We fully adhere to the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No. they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification:
The potential impacts are discussed in Appendix
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
28

---

Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), faireess considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied partieular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (c.g., gated release models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of MI).
11. Safeguards
Question:
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Answer: [NA]
Justification: Currently, this work does not pose risks for misuse.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
This paper builds off the the code base released by mmdDetection3d [10] (Apache 2.0 license), Nerf (39] (MIT License) and NeuS [55] (MIT License), which is cited in the introduction and implementation.
Justification:
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
29

---

If assets are released, the license, copyright information, and terms of use in the thcode. has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
package should be provided. For popular datasets, paperswi
latasets.paperssit
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: This paper does not have any new assets released.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/moodl as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: This work does not involve crowd sourcing or human research.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country institution) were obtained?
Answer: [NA]
Justification: This work does not involve crowd sourcing or human research.
Guidelines:
The answer NA means that the paper does not involve crowd sourcing research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
30

---

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: Our work does not involve LLMs.
Guidelines:
The answer NA means that the method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy (https: pttt::://neuris... /atps////coooeeeeeeee////////// for what should or should be described.
31


Reviews:
[{"review_id": "QfJz9FPvww", "reviewer": "Reviewer_1FNB", "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.", "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", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "[Strengths]\n\n- 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.\n- 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.\n\n[Weaknesses]\n\n- 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.\n- 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.", "was_revised": true, "final_justification": "All of my concerns about this paper have been well addressed. This cutting-edge work can inspire more ideas and research in the community, and I would recommend this paper to be accepted.", "rebuttal": "We would like to thank the reviewer for their thoughtful comments and questions. We especially appreciate the reviewer’s comments on the novelty of the work as being the first work to propose RF based object mesh reconstruction. Below, we will answer the concerns and comments brought up by the reviewer point by point.\n\n__The reviewer raises concerns of noticeable inaccuracies in the reference meshes.__\n\nThese inaccuracies are due to the fact that for specular objects, a line of sight reconstruction with vision is still a difficult problem. However, we believe the current baseline reference is a good comparison because it provides a general shape. It’s worth noting that in general RF reconstruction is much lower resolution, and hence as a first order, our goal is to reconstruct the high-level shape even if the minute details are not as accurate. \n\nThat being said, the reviewer raises a good point, and so we have rescanned the objects after painting them with a matte paint for the objects with very noticeable inaccuracies (bunny, elephant, bottle) and the results along with the updated quantitative results will be included in the revised paper. The changes in quantitative results are shown in the table below:\n| Object   | Method         | F1 Score | Chamfer Distance (cm) |\n|----------|----------------|----------|------------------------|\n| Elephant | Matched Filter | 0.59 –> 0.65     |0.39 –> 0.25          |\n|          | GeRaF          |0.84 –> 0.79     |0.13 –> 0.14                  |\n| Bottle   | Matched Filter |0.1 –> 0.44     |1.30 –> 0.35             |\n|          | GeRaF          |0.88 –> 0.97     |0.09 –> 0.05                   |\n| Bunny    | Matched Filter |0.49 –> 0.56     |0.40 –> 0.03            |\n|          | GeRaF          |0.79 –> 0.81     |0.1 –> 0.01                   |\n| Elephant* | Matched Filter | 0.47 –> 0.42     |0.60 –> 0.52          |\n|          | GeRaF          |0.58 –> 0.52     |0.46 –> 0.46                  |\n\n\n__The reviewer also raises concerns regarding the scalability and use cases of this work.__\n \nWe acknowledge that the training time is long, however, this is the first work in this area to tackle the issue of high resolution 3D mesh reconstruction using RF signals, which can open up many more applications than relying on visual models. While the current system as is, may not be the most efficient method for suspicious package detection or quality assurance, we believe that future work can push to lessen the gap to make GeRaF both scalable and more accurate. \n\n__The reviewer asks an interesting question regarding if the occluding object is metal.__\n \nThis is a limitation due to the radio frequency (RF) characteristics, since the RF signal itself would not be able to penetrate most metal surfaces. This means anything behind a thick metal surface would be effectively invisible. However, in other cases where the occluding surface does not reflect or absorb all of the RF signal (e.g. thin aluminum), then this loss in propagation is modeled by the incoming transmit power to the rendered scene. \n\nAgain we would like to thank the reviewer for their time in reviewing our paper and hope our answers clarify their uncertainties."}, {"review_id": "u2JcIHPt3h", "reviewer": "Reviewer_U8dg", "summary": "This 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.", "questions": "While 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?", "limitations": "Yes, the authors clearly address the limitations and potential negative societal impact of GeRaF.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- 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?", "was_revised": true, "final_justification": "The authors addressed the reviewer’s concerns,", "rebuttal": "We would like to thank the reviewer for their comments and thought provoking questions. We especially appreciate the reviewer’s comments on our novel methods to boost the computational efficiency in bringing neural reconstruction to RF, which opens up reconstruction in cases where vision fails. Below, we will respond to the comments and concerns raised by the reviewer.\n\n__The reviewer comments on the robust scanning strategies required to fully reconstruct.__\n\nThe reviewer is correct in the comment that robust scanning strategies or priors are still required in this work, however, we imagine future work will tackle the issue of adaptive scanning methods that make sure that we cover all viewpoints and/or will combine this work with generative models. However, we leave this issue to future work. In this work, we densely scan the object around 360 degrees to avoid missing patches. \n\n__The reviewer raises an interesting question regarding the stability of the three networks.__\n\nWhile training multiple coupled models (SDF network, Reflective network, Signal Power) as part of GeRaF, it is true that in some cases, instability may arise between the three networks. Asynchronous learning dynamics or gradient scale imbalance could cause one model (e.g., SDF network) to diverge or dominate the learning process. However, we take steps to mitigate potential instability by doing the following: \n\n1. The Reflective Network is designed with: Sigmoid output in (0, 1), which ensures bounded influence on any downstream loss (e.g., ray attenuation, visibility). Initialization to constant 0.5, which acts as a neutral prior (neither fully reflective nor fully absorbing) and prevents early overconfidence or masking of supervision signals.\n\n2. Signal Power Prediction is Not a Source of Instability. The signal power prediction model is a single parameter, not a high-capacity function approximator. \n\n3. The SDF model is initialized to represent a sphere [1], providing  a strong geometric prior, anchoring early training around a meaningful shape and gradients that are spatially structured, preventing early overfitting to noise or partial signals.\nAll three models interact through the end-to-end training pipeline and their contributions to the final signal are multiplicative or compositional, not adversarial. As a result, the reflective network scales the SDF-based surface responses, but does not compete with or override them and the signal power scalar normalizes the output, not drive dynamics. \n\n__The reviewer asks if any other baselines have been considered other than the Matched Filter.__\n\nIn the realm of radar imaging and reconstruction, for general 3D reconstruction methods, there are generally three common deterministic methods: Matched Filter [3], Beamforming [4], and Range Migration Algorithm [5]. However, it is important to note that with the exception of Matched Filter, the other two algorithms rely on assumptions that lead to inaccuracies as compared to the Matched Filter. For non-deterministic methods, the majority of state-of-the-art work is done on far-field reconstruction, where the assumptions on radar physics are completely different, and where small objects are not considered. Thus, the Matched Filter approach is the most accurate radar imaging reconstruction method that we can compare to. However, we will explore adding more baselines such as Beamforming[4], Range Migration Algorithm [5] and NeRO[2] (for a comparison with vision).\n\n__The reviewer also raises concerns on the advantages of using our method.__\n \nVisually looking at the comparison of the Matched Filter mesh and the GeRaF mesh, we believe there is a clear improvement in our results over the current methods used in radar imaging. For example, the nose of the elephant completely disappears in the Matched Filter mesh, whereas we can see the nose in our reconstruction. Another clear difference is in the bottle, where the neck of the bottle is shown in the GeRaF reconstruction, but not the Matched Filter mesh, showing the ability of GeRaF to reconstruct more complex/thinner portions of the objects which the Matched Filter meshes cannot. These show a clear advantage of GeRaF over current state of the art.\nWe would also like to specify that the Camera meshes shown in the results are all taken in line of sight (meaning the object is visible to the camera), however, many of our results also show the reconstruction in non-line-of-sight which the camera mesh completely fails (line of sight camera mesh is only shown for reference). Additionally, quantitatively GeRaF shows a large improvement over the Matched Filter result.\n\nWe do acknowledge that since this is the first paper to introduce neural implicit reconstruction for RF signals, there are still limitations in terms of practicality and accuracy of results to overcome in this system.\n\nWe again thank the reviewer for the thoughtful comments and questions, and hope that our answers clarify any concerns.\n\n[1] Atzmon, Matan, and Yaron Lipman. \"Sal: Sign agnostic learning of shapes from raw data.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.\n[2] Liu, Yuan, et al. \"Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images.\" ACM Transactions on Graphics (ToG) 42.4 (2023): 1-22.\n[3] Novak, Leslie M., Gregory J. Owirka, and Christine M. Netishen. \"Radar target identification using spatial matched filters.\" Pattern Recognition 27.4 (1994): 607-617.\n\n[4] Harter, Marlene, Andreas Ziroff, and Thomas Zwick. \"Three-dimensional radar imaging by digital beamforming.\" 2011 8th European Radar Conference. IEEE, 2011.\n\n[5] Zhu, Rongqiang, et al. \"Range migration algorithm for near-field MIMO-SAR imaging.\" IEEE Geoscience and Remote Sensing Letters 14.12 (2017): 2280-2284"}, {"review_id": "bk94sAwbOE", "reviewer": "Reviewer_t7Hp", "summary": "This 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.", "questions": "1. 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)", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strength\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.", "was_revised": true, "final_justification": "Authors has well addressed my concerns and I was convinced that the novelty proposed by this paper outweighs weakness. Therefore I am retaining my score as positive to borderline accept.", "rebuttal": "We would like to thank the reviewer for their thoughtful comments. We especially welcome the reviewer's appreciation of both the promising applications of our work but also the technical novelty involved in getting neural implicit representations to work with RF rendering, specifically lensless imaging and lowering the computational complexities to make training feasible. In the following paragraphs, we respond to the comments and concerns raised by the reviewer.\n\n__The reviewer highlights an important issue that the evaluation is only on our dataset.__\n\nUnfortunately, there are no publicly available radar datasets that scan objects from multiple viewpoints. Additionally, we would like to add that the Matched Filter baseline is the algorithm which maximizes the signal-to-noise ratio in the recovered image [3], which produces the highest quality image given there is no prior information of the scene. Further, there are no works, to our knowledge, whose methods are directly applicable to this problem. However, we believe that the comparison to Matched Filter is sufficient given that this is the current state of the art in radar imaging. Nonetheless, we plan to explore a comparison of all of our results with the Range Migration Algorithm [5], Beamforming [4] (two slightly faster, but less accurate radar based reconstructions) and NeRO [2] (reflective material vision reconstruction) for another radar signal processing and vision based comparisons. \n\n__The reviewer asks for further evaluation showing the effect of radar physics.__\n\nThe reviewer is correct, the effect of radar physics is a very important contribution to our work. We will include qualitative results on different scenes in the revised paper. For preliminary results we see that the elephant's Chamfer Distance increases from 0.14cm to 0.3cm and F1-Score decreases from 0.79 to 0.71 when radar physics are not taken into account. However, even more drastically, in the qualitative comparison the ears of the elephant completely disappear from the surface. We will include more results in the revised paper.\n\nWe would like to once again thank the reviewer for the time and comments. We sincerely hope that their concerns and questions are clarified. \n\n[2] Liu, Yuan, et al. \"Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images.\" ACM Transactions on Graphics (ToG) 42.4 (2023): 1-22.\n\n[3] Novak, Leslie M., Gregory J. Owirka, and Christine M. Netishen. \"Radar target identification using spatial matched filters.\" Pattern Recognition 27.4 (1994): 607-617.\n\n[4] Harter, Marlene, Andreas Ziroff, and Thomas Zwick. \"Three-dimensional radar imaging by digital beamforming.\" 2011 8th European Radar Conference. IEEE, 2011.\n\n[5] Zhu, Rongqiang, et al. \"Range migration algorithm for near-field MIMO-SAR imaging.\" IEEE Geoscience and Remote Sensing Letters 14.12 (2017): 2280-2284"}]

Author rebuttal:

Meta-review: Radio frequency (RF) signals offer the possibility to see through occlusions, such as cardbord boxes (see Figure 1), without causing harm as it is the case for X-Ray waves. This paper resembles the first work that shows how 3D reconstruction can be performed from RF signals for object-level meshes. This is of very high practical relevance (e.g. intrusion-free damage or threat detection) and likely to spur many follow-up works. 

The remaining concerns have been resolved and all reviewers vote for accepting the paper. Due to the postive reviews and the strong novel contribution, the AC recommends the paper for acceptance.
Decision: Accept (spotlight)