Non-Line-of-Sight 3D Reconstruction with Radar
Authors: Haowen Lai
Zitong Lan
Mingmin Zhao
Venue: NeurIPS 2025 poster ()
Keywords: Radar
NLOS Imaging
NLOS Scene Reconstruction
RF Sensing
3D Reconstruction
Primary area: applications
TL;DR: We present a practical system that reconstructs both line-of-sight (LOS) and non-line-of-sight (NLOS) 3D scenes using a single mmWave radar.

Abstract: Seeing hidden structures and objects around corners is critical for robots operating in complex, cluttered environments. Existing methods, however, are limited to detecting and tracking hidden objects rather than reconstructing the occluded full scene. We present HoloRadar, a practical system that reconstructs both line-of-sight (LOS) and non-line-of-sight (NLOS) 3D scenes using a single mmWave radar. HoloRadar uses a two-stage pipeline: the first stage generates high-resolution multi-return range images that capture both LOS and NLOS reflections, and the second stage reconstructs the physical scene by mapping mirrored observations to their true locations using a physics-guided architecture that models ray interactions. We deploy HoloRadar on a mobile robot and evaluate it across diverse real-world environments. Our evaluation results demonstrate accurate and robust reconstruction in both LOS and NLOS regions. Code, dataset and demo videos are available on the project website.

Non-Line-of-Sight 3D Reconstruction with Radar
Haowen Lai University of Pennsylvania molai@seas, upenn. edu
Zitong Lan University of Pennsylvania ztlan@seas upenn edu
Mingmin Zhao
University of Pennsylvania mingminzdseas. upenn. edu
httpa://vaves. seas. weeann.penn..... www.n.edu/ppoooeeeee ppeen.elu/projeetthholoaaaaa
Abstract
Seeing hidden structures and objects around corners is critical for robots operating in complex, cluttered environments. Existing methods, however, are limited detecting and tracking hidden objects rather than reconstructing the occluded full scene. We present HoloRadar, a practical system that reconstructs both line-of- sight (LOS) and non-line-of-sight (NLOS) 3D scenes using a single mm mawa radar. HoloRadar uses a two-stage pipeline: the first stage generates high-resolution multi-return range images that capture both LOS and NLOS reflections, and the second stage reconstructs the physical scene by mapping mirrored observations their true locations using physics-guides architecture that models ray interactions. We deploy HoloRadar on a mobile robot and evaluate it across diverse real-world environments. Our evaluation results demonstrate accurate and robust reconstruc- tion in both LOS and NLOS regions. Code, dataset and demo videos are available the project website.
Introduction
The ability to perceive beyond direct line of sight (LOS) is essential for robots operating in com- plex environments. Consider an autonomous vehicle approaching an intersection as children step unexpectedly into the street. If the vehicle can perceive the hidden scene and individuals before they become visible, it can and prevent accidents. Similarly, indoor robots navigating tight spaces can operate more safely and efficiently by reconstructing hidden structures and obstacles around corners, thereby allowing them to plan precise, collision-fire free paths in advance.
about their positions and motion, gaining precious time to react
Despite the importance of Non-Line-of-Sipee (NLOS) perception, visual sensors such cameras and LiDARs face fundamental limitations. These sensors operate at wavelengths on the order of hundreds of nanometers, which are much smaller than the surface roughness of common materials (e.g.. drywall, concrete). As result, light seatters in many directions, losing the directional coherence needed for imaging. In contrast, radio frequency (RF) signals have wavelengths orders of magnitude longer (i.c., millimeter or centimeter), causing most walls and structures to behave as specular reflectors like mirrors. This property enables RF signals to bounce predictably off surfaces and preserve geometric information even around corners. As illustrated in Fig. (a)) the returning RF echoes encode rich spatial cues about hidden objects and spaces, making RF uniquely suited for NLOS perception.
Existing NLOS imaging systems face significant limitations. Radar-based approaches often require prior knowledge of the scene geometry [28, 35, 38), or rely bulky hardware and long signal acquisition times [8, 34]. Single-photon LiDAR systems [2, 17, 24] involve expensive setups and suffer from spatial ambiguity due to diffuse reflections. Camera-based methods [3, 5, 30] analyze shadows for hidden object detection, but are highly sensitive to ambient lighting and cannot produce detailed images. More importantly, most systems [8, 28, 31, 34, 35, 38] are limited to localizing imaging hidden objects, rather than reconstructing the full 3D geometry of the occluded scene.
In this paper, we introduce HoloRadar, novel system that accurately reconstructs both LOS and NLOS 3D scenes with mm Wave radar. Achieving this capability requires overcoming several
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Spargff
Actual Locations
4
Range
M Wirorrd Points
First
Detocttion
Multiretum return RF Imaging
Signals
LOS
Second Stage:
Reflectio--wwer awere Scene Reconstrution
NLCS
Range
(a)
(b)
(c)
(d)
Figure 1: HoloRadar pipeline. It leverages (a) the multi noutti-boune RF reflections to reconstruct the scene. We
propose a two-stage pipeline for this task. (b) The first stage predicts the range (travel distance) of each bounce from the noisy and resolutioo signals. This emulates "mulli-return LiDAR" and extracts key points in the mirrored locations. (c) The second stage maps those mirrored points their actual locations and reconstructs the scene, (d) revealing hidden structures and humans.
technical challenges. First, radar sensors inherently suffer from low spatial resolution. Environmental reflections often appear as blurred blobs rather than distinct peaks. This is further exacerbated by measurement noise, signal artifacts, and the natural attenuation of signal strength. Second, multi- return reflections cause objects appear at mirrored locations in the radar's measurement space, where signals from multiple bounces are superimposed in complex ways. Mapping these mirrored observations back to their true physical locations requires accurate geometry and reasoning.
Our approach addresses these challenges using a two-stage pipeline. The first stage generates high- resolution multi-return range images by predicting multiple depth values per viewing direction. For each radar beam (i.e., ezimutthelevattion pair), the model estimates a of bounce distances, each corresponding to a reflection along multi-return path, as shown in Fig. ((b). This is analogous to what a multi-return LiDAR system would produce if all surfaces are specular. We train this model on noisy, low-resolution radar heatmaps using supervision from ray tracing over SLAM-bbsed scene meshes. While these multi-return range images capture geometric information from both LOS and NLOS regions, the recovered points reside in the radar's measurement space, where NLOS surfaces appear at mirrored locations due to multi-return propagation. The second stage resolves this ambiguity by reconstructing the physical 3D scene. We adopt physics-inspire models the ray tracing process. Specifically, we combine the predicted multi-return geometry with surface normal estimation at each bounce iteratively reverse the mirroring effect to put the mirrored points back to the world coordinates (Fig. 1cc)). This structured approach improves accuracy and accelerates convergence compared to end-to-end learning. Finally, we to correct residual geometric errors and fuse information across bounces, producing a coherent final reconstruction of the and hidden humans (Fig. (d))..
architecture that explicitly
refinement module
To evaluate our approach, we build a mobile robot prototype and collect data 32 corners across 5 buildings, with 28k radar measurements in total. We train our models on 24 corners except 8 for testing. For evaluation, we measure the Chamfer distance, Hausdorff distance, and F-score between our predicted scenes and the ground truth. Our method achieves overall LOS and NLOS F-score of 85.7% and 54.6%, with 18% and 35% better over baseline methods [9, 29].
In summary, the main contributions of our paper include:
We present the first practical system for NLOS 3D scene reconstruction around corners.
We propose a novel two-stage pipeline that decouples signal interpretation from spatial reasoning.
We conduct extensive experiments across diverse real-world environments and demonstrated accurate reconstruction in both LOS and NLOS regions.
2 Related Work
Radar Sensing and Imaging. Radar-based sensing and imaging have gained increasing attention in recent years due to their robustness in challenging environments [27, 39, 41]. Synthetic aperture radar (SAR) [10, 25, 26] a technique that enhances resolution by coherently combining measurements over time or space. Several systems leverage this technique with mechanical motion. Some move the radar along horizontal and vertical axes with sliders [11, [1], while others employ rotational movement
for panoramic sensing [20, 21]. For specific targets like humans [18, 22] or vehicles [11, 12, 23], machine learning models have been applied to further improve resolution. Recently, [20, 21] combine advanced signal processing with learning- based methods to support general purpose indoor imaging,

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recognition, and mapping. Nevertheless, nearly all these methods work with LOS reflections, with no applicability occluded or cluttered environments perception.
Radar-based NLOS Perception. Extending radar sensing to NLOS scenarios has been explored in several directions. Existing work [28, 38] localizes hidden humans by leveraging multi-hounce reflections, but it requires knowledge of the surrounding geometry. Similarly, [31] uses a LiDAR to determine the relay wall for outdoor 2D human detection and tracking. Mosaic [35], on the other hand, bypasses geometric modeling by installing retro-reflectors, yet the manual setup limits its gener- alizability. More recently, RFlect [8] produces coarse heatmaps of hidden objects using multi-bounce radar returns. However, it demands tune-consuming mechanical scanning to simulate a large synthetic aperture. There are also aarough-wall sensing efforts [42, 43] using low-frequency radar, though such approaches are ineffective for uround-comm scenarios where signals must traverse multiple barriers. short, current radar-based NI.OS methods are held back by deployment constraints and performance limitations. Moreover, none of these work aims to reconstruct the full 3D scene.
NLOS Sensing with Other Modalities. While cameras and LiDARs are widely used for LOS perception and reconstruction [19. 33, 37]. recent studies have explored their potential for NLOS sensing. Several methods [3, 5, 30] use RGB cameras to analyze indirect illumination and shadows cast on visible surfaces, enabling low-resolution reconstructions of hidden human activity. However, these methods require carefully controlled lighting and wall locations. limiting their robustmess in real-world conditions. Others [14-17, 24, 32] leverage single-photon avalanche diode (SPAD) LiDARs to capture two-bounce time-of-flight histograms hidden objects through transient imaging. While these systems can provide accurate measurements, they are typically bulky and expensive for mobile or daily deployment.
3 Radar Imaging Background
Radar transmits electromagnetic waves and receive reflections from objects and surfaces in the environment. To achieve sufficient angular resolution, radar systems typically use antenna arrays to synthesize a large aperture. In our setup, we follow the design of PanoRadar [20]. where a single-board radar is mechanically rotated to emulate a cylindrical array. As illustrated in Fig. 2(a), by compensating for signals received across antennas during a full rotation, we digitally form beams over No elevation angles (0) and N₂ azimuth angles (Φ), where & spans the full 360 field of view. To estimate depth along each beam, we use frequency frequency-modulated modulateed continuous wave (FMCW) waveform and apply a Fourier transform, resulting in NR discrete range bins. This process yields 3D heatmap H E IR No.No N which serves as a coarse volumetric representation of the environment. Higher values indicate stronger reflections and a greater likelihood of object presence.
Compared to camera images, radar heatmaps have significantly lower spatial resolution, and signal energy often spreads across neighboring bins [11, 23, 42]. However, radar offers a distinct sensing modality that interacts with the environment in fundamentally different ways. Due to its long wavelength, most surfaces behave as specular reflectors. As a result, signals often undergo multiple bounces before returning to the sensor, producing indirect reflections that appear at mirrored locations in the measurement space [8, 20]. While such multipath reflections are typically considered artifacts in conventional radar imaging, they carry valuable geometric information. In this paper, we build framework that models and exploits these indirect reflections to enable accurate NL.S reconstruction.
P
Beamforming
Heatmap
Rotate
No. Beams
No Beams
P2
R3
d,
d2
R2
11g
my
PL(P))
d,
R
1 bounce
Cylndrical Array
FMCW
n bounce
Signals
Ng Range Bins
3 bounce
Radar
(a)
(b)
Figure 2: System setup and muulti-buunce reflection. (a) Our rotating radar forms a cylindrical array with a 360 FOV. For signals collected in each cycle, beamforming is applied to obtain a 3D heatmap. (b) A 2D illustration of the multi-bounc reflection and notations.
3

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Equialant
Radar Heatmap
Range Images Y,
Points p
Actual Pr
Reconstructed M
First Stage: Mult-retum Imaging
Ray Tracing
Tracing
Refinement
Second Stage: R Reflection aware Scene Reconstructon
Figure 3: Network architecture of HoloRadar. We propose two-stage pipeline for radar based NLOS 3D reconstruction. The first stage aims to enhance radar imaging resolution and emulate 'mulli-return LiDAR", resulting range images each bounce. These images are equivalent to mirrored points. The second stage focuses on spatial reasoning, mapping mirrored points to their actual locations with a hybrid ray-tracing module, well as refining the reconstructed scene.
Method
In this section, first define our task and introduce notations ($ 4.1), and then show HoloRadar's two- stage decomposition network architecture ($ 4.2). This followed by explanation of our multi-return RF imaging model ($ 4.3) and reflection-aware scene reconstruction model ($ 4.4).
4.1 Problem Definition and Notations
Multi-return Reflections. The process of how a beam reflects illustrated in Fig. 2(b). Each beam may be reflected multiple times before returning to the radar along the same path. We use subscript i 2 1 to denote the number of bounces of a beam, and the prime symbol (") to indicate variables in mirror coordinate. Moreover, the beam direction for each bounce is defined d,, where dj is the initial beamforming direction which is known and fixed. The reflection assumed to perfectly specular with respect to the surface normal nę. We do not consider more than three bounces due to the decay of signal strength. To acquire ground truth, we use LiDAR SLAM to build a mesh map M for the environment.
NLOS Imaging Task. Our goal to reconstruct the environment M from the 3D heatmap H. Similar to an image in the mirror, the actual points p; in world coordinate will appear as pt in mirror coordinate in the measurement, as shown in Fig. 2(b). While mirrored points P lie in the same beam direction dj, the actual points p, can be anywhere depending the environmental structures, radar's location, and the number of bounces. This task is further complicated by the low-resolution noisy radar heatmaps as inputs.
4.2 HoloRadar Architecture
To tackle this problem. HoloRadar decomposes it into two sub-tasks, and adopts a two-stage structure to handle each one accordingly, as shown in Fig. 3. The first stage is multi-return RF imaging, which responsible for identifying the mirrored points p¹ from the heatmap that low-resolution with plenty of noise and artifacts. This can be formulated as range estimation task, as p = R;dı, where R, is the accumulated range (or travel distance) of beam up to the i-th bounce. If we estimate the accumulated ranges for every initial direction (di) (di)e,s, it will form three dense range images Y, (Re)a. NR,No Each pixel in Y; is essentially mirrored point p/. Fig. 3 visualizes an input heatmap and the predicted range images that correspond to mirrored point clouds.
The second stage is reflection-aware seene reconstruction, which is designed to reason about the spatial relationship between the real world and the mirror world and put mirrored points P back to their actual locations p,. To achieve this, we take inspiration from the physical reflection rules and introduce the ray tracing blocks with physical inductive bias. These modules work in cascaded manner, where the current ray tracing block takes the predicted reflected directions and surface normals from the previous block and outputs them for the next one. Hence, with the travel distance (accumulated range R;) of each beam from the first stage, the actual locations p, can be determined sequentially for every bounce. After this stage, points are placed back to the world coordinate. However, there are still some noisy and misaligned points due to the sequential prediction and error accumulations. We further apply a scene refinement module denoise the results and fuse points from all bounces for final scene reconstruction.

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Sparse Volume Encoding
Rey Tracing
Decoder
Range image
Ray Tracing
Location Ps
Y
Hybrid Est lock
Healmap
Ercoder
Decoder
Sparse
D2-1
Hybrid SN Refl. 8 llock
Shale
Range V2
Ray Tracing
P oint Location P2
Image
Concat
d
Hybrid Dir. Refl. 8 Block
Decoder
Ta Point Cloud
Range Image
Ray Tracing
Point
Location Pa
Naxt Bounce Egn. 33)
P
Final Scene
Y
Mulli-retum RF Imaging
Reflection
awaree aware Scene Reconstruction
Notations:
Y: Range Image n: Surface Normal Rellection Direction p: Point Location V:Sparse 3D Valume
Aborevitions: Concatenation (Concat), Surface Normal (SNI, Estimaton (Estl, Reflection (Refl), Direction (Dir.
Figure 4: Network Details. Left: HoloRadar adopts two-stage pipeline, taking an RF heatmap as input and predicting the scene around corners. Right: Details of the i-th ray tracing block and its inputs and oulputs.
4.3 Multi-return RF Imaging
The multi-return RF imaging model focuses on dense range estimation, aiming to predict the range images Y, E R.NoxxN..i 1,2,3 from the 3D radar heatmap H No XNn, To process the heatmap, we adopt the strategy of treating the range dimension channels and apply 2D CNNs across elevation and azimuth dimension, an approach shown to effective for enhancing imaging resolution in prior work [20]. This design is well-suited to address the spatial energy spreading caused by the limited sensing resolution while remaining computationally efficient.
Building on this, we develop a UNet-based architecture with shared encoder, separate decoders, and skip connections, as illustrated in Fig. 4. The encoder processes the full heatmap H and extracts shared RF features, which are then passed through three specialized decoders to reconstruct the respective range images Y, for each bounce. Our insight is that while LOS and NLOS regions often share signal patterns caused by similar environmental structure, they still differ in several aspects such as strength, noise characteristies, and energy spreading. Thus, instead of regressing a multi-channel image directly, we design separate decoders to better capture the distinct signal characteristics of different bounces. With the predicted range images Y, we compute the loss combination of per- per-pixe range estimation error and perceptual error:
Ling Avange El x: + Alpips Σ LPIPS Y, Y.).
(I)
where Xrange, Alpips weighting factors, and LPIPS(.,) :) computes the perceptual loss using LPIPS metrics [40]. The three branches are jointly optimized, and the perceptual loss is used for capturing details objects such humans. We obtain the ground truth Y, by performing ray tracing the environment mesh map M, with initial directions the same as the beamforming directions d).
4.4 Reflection-aware Scene Reconstruction
After obtaining the range images Y: = (R;) )o.s- s the reflection- -ware scene reconstruction model first maps every mirrored points P = R,d; back to their actual location p: using our ray tracing modules. These modules work in cascaded manner and sequentially predict the actual points. Then, it refines all p. to reconstruct the final scene in volumetric representation, and converts it to point cloud for visualization. Fig. shows the detailed structure of the model.
Ray Tracing. This module simulates how ray bounces off surface and change its direction, assuming perfect specular reflection. Given the previous incoming ray direction d,-1 and the surface normal n at point Pi-1, the current outgoing reflection direction d, can be determined by:
d; = d - 2(d;-1 n;-1)n;-1-
The reflected ray then travels along the current direction d, for distance R2 R;-1 until it hits surface, giving the current hit point p, as:
pj Pi-1 (R, R2-1)d.
(3)
5

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Equations (2) and (3) show that the mirrored points P = R,d from multiple bounces can be iteratively mapped to their actual location. Therefore. we duplicate the ray tracing module three times and cascade them for the prediction of all the points in world coordinate.
During ray tracing, the surface normal n. is unknown and needs estimation. For range image Y; = (R.),.g. the surface normal at direction (0, Φ) has a close-form solution [1] as:
nf = Re. by, b, = R, cas
aR. dp
anL
R. 09
where Rø a pre-computed rotation matrix for every direction (0, o) and b, a geometry vector depending on the local surface structure. Note that the normal vector n' in Eqn. (4) is mirrored version of the true surface normal n,, because Y; represents an image of the mirrored world. To recover the actual normal n; in the world coordinate, we apply reflection operation using the previously computed normal 1;-1:
(5) For implementation purposes, we manually initialize Y, = ny = Pu = 0, and set do = d1. which is the known and fixed beamforming directions.
n, n= D - 2(n' n.-1)n
Hybrid Estimation Blocks. Since surface normal calculation in Eqn. (4) involves derivative, is sensitive to noise and errors in the range images. However, accurate surface normal is critical for determining the next bounce point. That is because an error in surface normal will result in incorrect reflection directions, and this error will be further magnified for longer distance. To address this issue, we add a residual neural network branch for Eqn. (2)(4)(), extracting features directly from the input. These input similar images but with different channel dimensions, representing the surface normal directions (n;-1) and range (Y,). The extracted features and analytical results are concatenated and fused together with fusion blocks. Both the residual branch and the fusion branch are made of standard residual blocks [13]. Structure details can be found in Appendix & C.2. Ablation study in 5.4 demonstrate the effectiveness of our hybrid learning design.
Refinement and Scene Reconstruction. After ray tracing, the predicted points Pa are now in world coordinates. However, some of them may not align precisely with the geometry. To address this, we incorporate a voxel-based 3D UNet that fuses predictions from different bounces and refine the reconstruction with volumetric representation. Given that most voxels are empty (~90%), we adopt sparse 3D convolutions from Minkowski Engine [7] to reduce computation cost and memory usage.
We formulate scene reconstruction as hybrid multi-clase classification and regression task. Each in- put point associated with vector (z,y,2,Aa) Ay,Ar,i,n), where (z.y.z) are the point coordinates. (Ar,Ay,Az) are offsets to the voxel center, i is the bounce index, and is the number of points falling into the voxel. A voxel encoder projects this value into feature vector, which is then averaged to obtain the voxel feature when multiple points fall into the same voxel. Bounce index i is included to help the model assess the reliability of each point, since different bounce point has various noise distribution. The model predicts a classification volume Vels and regression volume V reg.- We distinguish three semantic classes: empty space, humans, and structures. The final 3D positions of all non-empty voxels are obtained by adding the regressed offset to their corresponding voxel centers.
Model Training. We supervise both the ray tracing and the refinement module to improve perfor- mance and accelerate convergence. In the ray tracing module, instead of directly supervising the predicted surface normal ñi; we compute loss the corresponding geometry vector b, against the ground truth b, We also use a per-point LI loss between the predicted points p. and the ground truth points p,. The total loss for ray tracing is defined as:
LRT = Asn Σ lb. bull1 + Apoint Σ -P.lI-
For scene refinement module, we apply cross-entropy loss to the classification prediction Vele and an LI loss to the offset regression prediction V reg. To encourage the predicted non-empty voxels to align closely with surfaces, we introduce a distance loss. Specifically, we apply a Euclidean Distance Transform (EDT) [4] to the ground truth volume Vels. which assigns each empty voxel with distance to the nearest non-empty voxel. The total loss for refinement becomes:
Crefine CE( Vela + Areg V/
reg V Vregl1z + Adist VeL EDT(
(7)
where Vela and Vegg denote the ground truth classification and regression volumes, respectively, and the symbol * denotes element-wiss (oer-voxel) multiplication.

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LIDAR
Setup
Radar
Neret
Racar yystem
RobotLOS View
Figure Experiment setup. (a) Example corners in the dataset. (b) Our radar system and robot. (c) Around- corner setup. (d) The human occluded from the robot's LOS view.
1 B"Bunce Prediction
Bounce Ground Truth
2dd Bounce Prediction
Bounce
G Ground Truth
Bounce Prediction
Rounce
Example
ZoomedHin
Example
Zoomed-in
Example
Figure 6: Multi-return RF imaging results. Our model successfully reveals NLOS structures and human (highlighted and zoomed in by white boxes) in both the 2nd and 3rd bounce images.
Experiments
5.1 Experimental Setup
Dataset. We collect a dataset from 32 distinct corners across buildings, which are constructed between 1906 and 1996 and renovated between 1973 and 2017. We use 24 corners for model training and the remaining 8 for evaluation. As illustrated Fig. 5(a), our dataset includes diverse corner layouts, including 21 T-shaped, 5 L-shaped, cross-shaped, and oblique corner at 45". Corner width ranges from 1.33 m to 4.63 m, with a mean of 2.16 m and a standard deviation of 0.89 m. In each corner, positioned human subject behind the corner, out of the direct LOS, to simulate realistic NLOS imaging conditions. Fig. 5(b) shows our radar system, and examples of dataset collection scenarios depicted in Fig. 5(c) and (d). Both the human and the robot are free to move, leading to total of 28k distinct RF heatmap scans. To capture humans located in regions invisible to LiDAR, recorded videos using stereo depth camera, and then aligned the camera point cloud with the environmental mesh using point cloud registration [6].
Evaluation Metrics and Baselines. We employ comprehensive depth error metrics, including mean (cm), median (cm), 90-percentile error (cm), and PSNR (dB), to evaluate the performance of the first stage, multi-return RF imaging. In the second stage of scene reconstruction, we assess the results using Chamfer Distance (CD, cm), Modified Hausdorff Distance (MHD, cm), and F-score (F-S., %), which quantifies geometric agreement between predicted and ground truth point clouds. We note that existing baser-based sec-around-come -ornerr methods neither predict dense multi-return images nor reconstruct the full scene, therefore unsuitable for direct comparison. To provide fair baselines, implemented two vision transformer models, VIT [9] and DPT [29]. Details on the evaluation metrics ($ B.4) and the baseline implementation ($ C.3) can be found in the appendix.
Implementation Details. individually train the models the two stages. For multi-return RF imaging, both encoder and decoder architectures consist of 7 layers, with each encoder layer containing 4 residual blocks and each decoder layer containing 2 residual blocks. We trained this model using batch size of 8 for 90k iterations. In reflection-ane scene reconstruction, our hybrid

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KK
 1
Scono Recorstouztion
Gourd Trudn
Ours
LOS
NLOS
Comer Detail
Ground
Example 1
Example 2
Example
Example 4
Example 5
Figure Scene reconstruction results. Top two rows: full scene reconstruction. Bottom two rows: detailed views each corner Green and purple points represent LOS and NLOS geometries, respectively, while dark-purple points indicate the hidden person.
estimation blocks incorporate 4 residual blocks each in both the residual and fusion branches. Our refinement model adopts 5-layer 3D UNet structure, with each layer composed of 2 residual blocks using sparse convolution in both encoder and decoder stages. The scene space is set as 20 m x 20 3.5 m and partitioned into 256 256 32 voxels. We train this model with a batch size of for 60k iterations while freezing the first stage. All experiments use the Adam optimizer, incorporating a warm-up period of Ik steps and an initial learning rate of 10 following cosine annealing schedule. All models are trained on an NVIDIA L40 GPU.
5.2 Results
Multi-refurn RF Imaging. We present the results multi-return RF imaging in Tab. 1, comparing range estimation errors for each bounce individually. The results indicate that our UNet-based multi-return RF imaging model achieves superior range estimation accuracy across all three bounces. As shown in Fig. 6, the predicted range images align closely with the ground truth. Notably, in both the second- and horrdbbunce cases, our model accurately reconstructs depth maps, capturing the hidden human silhouettes (in white boxes) that closely match the ground truth.
1st Bounce
2nd Bounce
3rd Bounce
Method
Mcan,
Median,,
90th.
PSNR
Mcan,,
Median,,
90th.].
PSNR 1 Mcan, Median +. 90th
PSNR 1
9.81
2.69
19.83
31.37
24.30
9.44
57.15
25.03
40.61
17.68
98.08
22.11
DPT
8.87
2.37
16.68
31.44
23.00
8.45
51.48
24.88
39.27
15.94
87.09
21.64
Ours
7.03
2.02
12.33
32.83
19.03
7.17
40.05
25.98
31.36
13.72
73.95
22.58
Table 1: Performance of the multi-retumy RF imaging model.
3D Scene Reconstruction. After training the first stage, we ]train the reflection-aware scene reconstruction module and evaluate its performance. As shown in Tab. our two-stage pipeline consistently outperforms end-to-end transformer baselines across all metrics, both LOS and NI.OS regions. These results validate that: (i) dccomposing the task into distinct imaging and spatial reasoning stages and (ii) incorporating a physics-guided ray-tracing bias significantly enhance model generalization. Even prior to the scene refinement stage, our hybrid ray-tracing module already surpasses the best baseline. The subsequent scene refinement further aligns the reconstructed point cloud with the ground truth, improving the LOS and NLOS F-scores by approximately 18% and 35% over competing methods, respectively. Fig. 7 presents qualitative results of reconstructed scene geometries from various test corners. The predicted reconstructions align closely with the ground truth, accurately capturing both hidden humans and surrounding NLOS structures. This demonstrates that our method effectively extends perception from direct LOSS to occluded NLOS regions. Comparisons with baseline methods shown in Fig. 8, where approach produces

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LOS
NLOS
Method
CDL
MHD]
F-S.f
CD1
MHD
F-S.t
End end VIT
End end DPT
End2end ViT
19.8
13.1
72.4
45.4
28.5
40.4
End2end DPT
21.5
14.6
69.8
50.3
31.1
37.0
w/o Refine.
13.8
7.3
83.8
43.5
29.3
50.3
w/ Refine.
12.2
7.1
85.7
40.0
28.3
54.6
our
Ground Truth
Figure 8: Visual comparison with baselines.
Table 2: Results for the final 3D scene reconstruction.
clearer and more complete reconstructions both LOS and NLOS areas, while the basclines exhibit noisy or incorrect structures (c.g., missing humans or closed corridors).
Human Detection and Localization. Our system predicts human labels the reconstructed point cloud, enabling hidden human detection and localization. Detection performance is evaluated using a 50 em matching threshold, where detected human points are aggregated into centroids and compared to ground-truth positions using Euclidean distance. Our method achieves a detection precision of 99.96%, recall of 97.29%, and F-score of 93.48%, with mean localization error of 13.77 em and median of 12.55 cm. The high detection rate and low position error demonstrate strong performance in NLOS human detection and localization, allowing robots navigate safely around cornes.
5.3 Analysis on Corner Diversity and Signal Attemuation
To evaluate the robustness and generalization of our sys- tem, we test NLOS reconstruction performance across corners with varying shapes, sizes, and signal attenuation levels. The results are summarized in Tab. 3.
Variation
CD1
MHD]
F-S.t
T-shaped
34.1
23.9
61.9
Shape
Ctoss-shaped
37.8
24,9
47.8
L-shaped
45.3
32.7
52.5
Corner Shapes and Sizes. Corners are categorized by shape (T-shaped, cross-shaped, and L-shaped) and by size (narrow: 1..5 m, medium: 1.5-3 m, wide: >3 m). We observe that T-shaped corners yield the best performance,, likely due their larger reflective surfaces. which facil- itate stronger multi-bounce signal propagation. Cross- shaped and L--haped corners, with more irregular geome- tries, show slightly reduced accuracy. Among the size categories, medium-widie corners achieve the lowest re- construction errors. This is because narrow corners may introduce stronger multipath interference, while wide corners can weaken reflections due to longer propagation paths and greater beam dispersion. Nevertheless, the observed variation remains modest, approximately 10 cm in Chamfer and Hausdorff distances, and around 10% in F-score, indicating that our system maintains accurate even under structural irregularities.
Narrow
39.1
27.5
57.6
Size
Medium
33.0
21.7
57.5
Wide
42.5
32.8
59.2
Signal
High
39.4
27.1
53.1
Medium
37.2
26.4
55.9
Atten.
Low
35.1
25.5
66.1
Table 3: NLOS reconstruction performance by different corner shapes, sizes, and signal attenuation levels.
Signal Attemuation Levels. Different materials exhibit varying levels of RF signal attenuation. To quantify attenuation, we sample the radar heatmap in the L.OS region and compare it with the energy observed in the hidden target region, measuring the power drop between L.OS and corresponding NLOS reflections. Based on this measure, the test data are divided into three bands: low attenuation (<10 dB), medium attenuation (10-20 dB), and high attenuation (>20 dB). expected, the system performs best in low-attenuation conditions. However, even under substantial signal loss, it maintains low Chamfer and Hausdorff distances and a high F-score. These results demonstrate the resilience of our method to challenging reflective conditions, and the robustness in diverse environments.
5.4 Ablation Study
Model Architecture for Multi-return RF Imaging. We study the impact of different encoder and decoder layers on multi-return RF imaging performance, as summarized in Tab. Each configuration is defined by the number of shared encoder layers, separate decoder layers, and shared decoder layers. The total number of decoder layers (separate plus shared) kept equal to the number of encoder layers for consistency. Deviating from our chosen configuration, by either reducing or increasing the layer count, leads to diminished or saturated performance. The last three rows of table further analyze the effect of increasing bounce-specific decoder layers while keeping the encoder depth fixed. Results show that adding separate decoder layers for each bounce consistently improves accuracy across all bounces, supporting our design choice discussed in $ 4.3.

---

1st Bounce
2nd Bounce
3rd Bounce
Settings
Mean
Median,
90th
PSNRT
Mean]
Median)
90th
PSNR
Mean
Median
90th L
PSNR 1
(4,4.0)
8.15
2.17
14.74
31.58
22.02
7.68
47.37
24.76
35.95
14.76
86.61
21.47
(7,7,0)
7,03
2.02
12.33
32.83
19.03
7.17
40.05
25.98
31.36
13.72
73.95
22.58
(10,10.0)
7.14
2.06
12.67
32.72
19.11
7.05
40,30
25.86
31.28
13,51
74.18
22.62
(7,0,7)
7.61
2.23
13.51
32.33
19.41
7.27
41.34
25.92
31.64
13.84
76.10
22.54
(7.3.4)
7.25
2.09
12.88
32.67
19.07
7.09
40.24
25.86
31.40
13.78
74.34
22.55
(7.7.0)
7.03
2.02
12.33
32.83
19.03
7.17
40.05
25.98
31.36
13.72
73.95
22.58
Table 4: Ablations for multi-return RF imaging. We evaluate different configurations of (#encoder layers,
#separate decoder layers, #shared decoder layers). Colored lines are our default configuration.
Module Design for Ray Tracing. As shown in Tab. 5, we evaluate various designs for our hybrid ray-tracing module, including pure signal processing (SP only), pure neural networks (ML only), and our proposed hybrid approach. Our hybrid method signif- icantly outperforms either the pure signal processing or pure neural network approaches, validating the discussion presented in $ 4.4.
Study objectives
Variation
NLOS F-S.1
SP only ML only
16.2
Ray tracing
40.4
w/o distance loss
29.3
Scene refinement
w/o offscts
50.7
Our implementation
54.6
Table Ablations for the reflection-awe aware scene reconstruction model.
Design of Scene Refinement Module. We further
analyze the scene refinement module by varying the loss function and network output targets, as detailed in Tab. 5. Introducing a distance-based loss significantly improves the F-score, demonstrating that this loss encourages predicted voxel locations more accurately align with ground truth. Additionally, predicting continuous voxel coordinates rather than discrete quantized locations helps mitigate errors due to spatial quantization.
3D Convolution Efficiency. To assess our choice of sparse 3D convolution, we evaluate the trade-off between reconstruction accuracy and computational efficiency across different convolution designs. We compare dense 3D convolutions at multiple feature resolutions with our sparse convolution model. All inferences are performed on an NVIDIA RTX 4070 GPU, and results summarized in Tab. 6. The sparse 3D convolution achieves substantially faster inference than full-resolution dense convolution while maintaining comparable accuracy. Although it requires an additional rulebook, the inherent sparsity of the scene significantly reduces computation time, validating this design choice. The end-to-end inference time is 298 ms (38.4 ms for multi-retumn imaging and 80 ms for ray tracing), which is within the radar's 's 0.5 s rotation period and supports real-time operation.
Design choices
Infer. time (ms)
GFL.PPS
#Parameters
NLOS CDJ
NLOS F-S.
Full-resolution dense 3D conv 2x downsampled 3D conv
274
622.24
54.7 M
37.5
53.7
121
322.33
54.7 M
37.6
47.1
4x downsampled 3D conv
40
247.61
54.7 M
42.7
29.9
Sparse 3D conv (ours)
180
225.05
53.1 M
40.0
54.6
Table 6: Runtime and NLOS reconstruction performance for 3D convolution design choices.
6 Discussions
Conclusion. We introduced HoloRadar, radar-based system capable of reconstructing both LOS and NLOS 3D scenes around corners. Leveraging two-stage learning and physics-guide guided framework that decouples signal interpretation from spatial reasoning, HoloRadar accurately reconstructs hidden geometry and human subjects with high fidelity. Extensive evaluations on a real-world dataset covering diverse corner scenarios validate the effectiveness and robustness of the proposed approach. By enabling full-scene reconstruction beyond direct visibility, HoloRadar advances radar sensing toward broader spatial perception. This capability supports safer autonomous navigation, improved situational awareness in search and rescue, and more effective human-robot interaction in complex or visually occluded environments.
Limitations. As an initial step toward NLOS 3D scene reconstruction, this work focuses primarily on indoor ground-corner scenarios. Extending the system outdoors would introduce challenges such as longer signal propagation distances and interference from fast dynamic objects. However, the proposed two-stage pipeline can be readily adapted to radar systems designed for outdoor operation. In addition, the current scene refinement module lacks an explicit human-shape prior. Integrating parametric human body models could further enhance the fidelity of reconstructed human geometry.
10

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Acknowledgments and Disclosure of Funding
We sincerely thank the reviewers for their insightful comments and suggestions. We are grateful to Zhiwei Zheng for the discussion, and to Running Zhao and Xin Yang for their help in data collection. This work is supported by University of Pennsylvania with no competing interest.
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A Additional Experimental Results
We provide further experimental comparisons between baseline methods and our approach. Fig. 10 illustrates the multi-return RF imaging results for each bounce. Our predictions align closely with the ground truth, whereas baseline methods exhibit several noticeable artifacts, marked by gray boxes. Specifically, examples and highlight incorrect geometric predictions, example demonstrates a missed detection of a human subject, and example indicates an additional, incorrect detection. These inaccuracies can negatively impact subsequent scene reconstruction tasks. The comparison clearly shows the robustness and effectiveness of our design.
Additional scene reconstruction results are showcased in Fig. 11. Baseline methods achieve rough geometry reconstructions but generate significant noise, particularly at greater distances. Additionally, these methods consistently fail to detect humans located around corners, limiting their real-world applicability for robotic tasks. In contrast, our method produces clean and accurate reconstructions of both LOS and NLOS scenes, and reliably detects humans in NLOS regions. This outcome underscores the effectiveness of our proposed two-stage approach compared to end-to-end methods.
B System Implementation Details
B.1 Radar Configuration
Our system employs millimeter-watt (mm (ameave) cascaded radar, specifically the TI MMWCAS- RF-EVM combined with MMWCAS-DSP-EVA data capture board, transmit radio signals and receive environmental reflections. The radar frequency sweeps from 77 to 81 GHz, resulting in 4 GHz bandwidth and a range resolution of 3.75 em. Each chirp consists of 256 samples, allowing a maximum sensing range of 9.6 m.
The radar mechanically rotated by stepper motor frequency of Hz with rotation radius of 10.5 cm. For the raw radar measurements collected over a full cycle, they will be processed into an RF heatmap, which serves the input to the neural network. During signal processing, beamforming conducted over 64 elevation angles spanning [-45",45 445) and 1024 azimuth angles covering 360 The data is further downsampled by along the azimuth dimension, resulting in a final 3D heatmap H of dimensions 64 612 256 (elevation, azimuth, and range respectively). The heatmap retains only amplitude data and omits phase information, since amplitude directly indicates reflection strength. example heatmap is shown in Fig. 9a), where we select a 2D range-azimuth slice at zero elevation.
B.2
Data Collection
To establish ground truth, our setup includes Ouster 64-beam LiDAR sensor alongside the mm mm Wave radar. Both sensors are mounted on a Lynxmotion Wheeled Rover mobile robot platform, which manually operated using joystick.
During data collection, a human subject stands on one side of corner while the robot positions itself on the opposite side, with both entities free to move. As the human is obscured by the corner in NLOS, we use a ZED 2i stereo depth camera to record visual data of the subject. The stereo videos are further processed to give camera point clouds. Radar, LiDAR, and camera data are collected simultaneously and synchronized using timestamps. Our dataset comprises data from 32 different
Azimuth
Azimuth
Range
(b) Difference between consecutive heatmap frames Figure 9: Visualization of RF heatmaps. (a) A 2D range-azimuth slice at zero elevation of an example RF
(a) An example heatmap (2D slice)
heatmap. (b) The absolute amplitude difference between consecutive heatmap frames.
14

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corners, each containing roughly 875 synchronized data collection cycles, totaling approximately 28k samples. To simulate real-world variability, some data are collected without human presence.
To capture variation across scans of the same corner, data are collected with both a moving robot and moving human, causing sensor and target positions vary across acquisitions. At millimeter-wan frequencies, even millimeter-scale position changes can shift the signal phase, leading to noticeable differences in the beamformed RF heatmaps between scans. Fig. 9(b) illustrates these effects, showing the absolute amplitude difference between consecutive frames.
B.3 Ground Truth Acquisition
Ground truth data for LOS and NLOS scene reconstruction generated using customized ray tracing pipeline. Initially, we leverage 3D LiDAR SLAM algorithm to generate a detailed geometric mesh map of each corner without human presence. Then, we register point clouds from the depth camera to the pre-built mesh map to determine the initial camera pose. This is one-time calibration since the camera is stationary. LiDAR point clouds similarly registered, but it is performed dynamically for each rotation cycle due to robot movements. This ensures consistent spatial alignment of both robot and human subjects within the shared map coordinate system.
then simulate beamforming directions through ray tracing. For each ray cast, we record travel distances at every bounce, generating accurate ground truth multi-return range images. Additionally, surface normals, semantic labels (LOS, NLOS, human, non- non-human), and spatial coordinates (x, y, z) of each intersection point are computed and stored.
B.4 Evaluation Metric
For reconstruction results, we evaluate the similarity between predicted point cloud P and its ground-truth counterpart G using three metrics: the Chamfer Distance (CD), the Modified Hausdorff Distance (MHD), and the F-score distance threshold т.
Chamfer Distance. The Chamfer Distance measures the average closest-point distance between two point sets. Given P 1 and G {gg 1
it is defined
CD(P,
1 N
Σ
min |p
g|l2 +
1 M
min ||g PEP
pl12.
(8)
gEG
PEP
9EG
This symmetric form accounts for both point sets equally.
Modified Hausdorff Distance. The Modified Hausdorff Distance captures the worst-case average deviation between two point clouds. It is defined as:
MHD(P, G)
1 N
min Ip g12r 9EG
1 M
Σ min llg
pll2
PEP
9EG
Unlike the classical Hausdorff distance, which takes the maximum of all pairwise minima, the modified form uses the mean distance in each direction, making it more robust outliers.
F-score. The F-score measures the harmonic mean of precision and recall based on distance threshold т. The precision and recall between two point clouds P and G are defined as:
Hp P mingeg |p alle 7
precision
(10)
P
Hg E G
minpep [|g plla < <T)
recall
(11)
G
Then the F-score is computed as:
F-score precision recall
(12)
precision + recall
This metric reflects the balance between completeness and accuracy of reconstruction, where higher F-score indicates better geometric alignment the specified tolerance. our evaluation, the threshold 7 is set to be 10 em, aligning with the voxel size.
15

---

1 Bounce
2 Bounce
3 B ounne
E Eample 1
GT
VIT
DPT
Example 2
Ours
VIT
DPT
Exarple
Ours
Exaiple
Figure 10: Multi-return RF imaging results. We compare method with baseline approaches for each bounce. Gray boxes highlight artifacts the baseline predictions.
Together, these three metrics provide comprehensive evaluation of geometric fidelity between pre- dicted and ground truth point clouds, balancing average distance, structural deviation, and threshold- based overlap.
16

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End-to-end VIT
End-to-end DPT
OUIS
Ground Truth
Human
Example
LOS
NLOS
Example
Figure 11: Scene reconstruction around corners. We compare our method against two end-to-end tramsformer- based baselines. Green, light purple, and dark purple points represent LOS geometry, NLOS geometry, and NLOS human, respectively. For all examples, the ceiling and floor are omitted for clearer visualization.
C Network Implementation Details
C.1 Multi-return RF Imaging Model
Our multi-return RF imaging model employs a UNet-based architecture featuring a shared encoder, three separate decoders, and skip connections. Both encoder and decoder modules contain 7 layers built from standard residual blocks [13]. Each encoder layer comprises 4 residual blocks, whereas each decoder layer includes residual blocks. At the end of each encoder layer, feature maps are downsampled by factor of 2, and correspondingly upsampled at each decoder layer. To maintain sufficient resolution along the elevation dimension, downsampling this direction stopped after the fourth layer. Skip connections bridge corresponding layers between the encoder and decoders. Additionally, an initial stem layer using 7x7 convolution reduces input channels by a factor of 4. This initial convolution effectively captures the energy leakage pattern along elevation and azimuth from the low-resolution RF heatmap while compressing sparse signals along the range dimension.
The model is trained with a batch size of 8 over 90k iterations using the AdamW optimizer. The optimizer uses parameters 31 0.9 and |3 0.999. The learning rate is initiated 10-4, with 1k step warm-up and cosine annealing schedule. Data augmentation involves random rotations and flips along the azimuth dimension, and per-element Gaussian noise addition to the input heatmap. The heatmaps are log-scaled and normalized to [-1, 1] to mitigate extreme value ranges.
C.2 Reflection-aware Scene Reconstruction Model
Ray Tracing. Our ray-tracing sub-module utilizes hybrid estimation blocks to accurately predict surface normals and reflection directions. There are three types of hybrid estimation blocks: hy- brid surface normal estimation block, hybrid surface normal reflection block, and hybrid direction reflection block, as illustracted in Fig. 12. Each hybrid estimation block includes two branches: physics-guided signal processing branch indicated by the corresponding equation, and residual fea- ture extraction branch with residual convolution blocks. While Eqn. (2)-(5) describe operations
17

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Hybrid SN Est. block
Hybrid SN Refl. block
Hybrid Dir. Refl. block
Y,
Eqn.
Res
Blocks
n
Eqn.
Eqn. (2)
Res Block
Res Blocks
Res B lockss
Res Blocks
Notation:
V: Range Image n: Surface Normal d: Reflecttion Direction p: Point
Figure 12: Structure of the hybrid estimation blocks. Fach of these black contains a physics-guiddnd process indicated by the equation, and a learnable residual feature extraction branch. These feature maps will be concatenated and fused together for the final output (e.g.. surface normal reflection direction).
the level of a single pixel or point, these operations can be applied to the entire image, enabling batch processing that produces surface normals, reflection directions, and point locations as image-like tensors with (x, y, 2) as channels. These image-like representations are then processed by the residual feature extraction branch through convolutional layers. The resulting feature maps are concatenated with outputs from the signal processing branch (i.e., Eqn. (2)-()) along the channel dimension before being passed to the fusion branch, which consists of 2 residual convolution blocks.
Training is conducted with a batch size of 4 60k iterations, using the same optimization strategy as the multi-return RF imaging model. Data augmentation includes random rotations and flips along the azimuth dimension. This sub-module is trained independently and its parameters will be frozen for the subsequent refinement sub-module training.
Refinement and Reconstruction. The refinement sub-module features voxel-based 3D UNet architecture utilizing sparse convolutions, comprising layers each with 2 residual blocks both encoder and decoder. Spatial dimensions of 20 m mx3.5 voxels. To ensure sufficient resolution, downsampling along the z (height) dimension stops after reaching voxels the third layer. Classification and regression heads contain one residual block followed by a 1x1 convolution to adjust output channels. Given the voxel-based nature of this sub-module, the F-score threshold is set the voxel size (10 cm). Training parameters identical to those of the ray-tracing sub-module.
discretized into
C.3 Baselines
We implement two wansformer-based baselines: end-to-end VIT and end-to-end DPT. Both of them use identical training parameters, optimizer settings, and augmentation methods as our multi-return RF imaging model.
End-to-end VIT. Our Vision Transformer (ViT [9]) based baseline partitions the RF heatmap into non-overlapping patches of size 4x8 along elevation and azimuth, while retaining all range channels. This operation similar to the standard technique applied to RGB images. Each patch flattened and linearly projected into fixed-size tokens. We adopt the ViT-Base architecture in [9] (i.e., 12 layers, 768 hidden size, 3072 MLP size, 12 heads). An output head decodes each token into a -in-dinnnsional vector reshaped to 9x4x8, representing xyz coordinates across 3 bounces and 4x8 8 beam directions per patch. For training, it employs per-point LI loss against ground truth.
End-to-end DPT. The Dense Prediction Transformer (DPT (29]) based baseline follows a similar patch embedding and transformer configuration as the VIT baseline. Different from [29], integrates 6 Reassemble blocks at layers [2, 4, 6, 8, 10, 12]. These blocks adjust feature maps to dimensions
No No with [81, 52] sequentially set [[2.2], [2,4], [4,8], [4,16], [8.32], [8,64]]. Similar to F2
our multi-return RF imaging model, three separate decoders predict point location images for each bounce. For fusion blocks in the decoder, we use feature concatenation instead of direct addition. Per-point I.1 loss is used during training.
D Social Impacts
The proposed system has significant positive social impacts, primarily enhancing safety and reliability in robotics and autonomous systems. By enabling machines to perceive hidden humans and structures around corners, our approach can substantially reduce accidents involving autonomous vehicles. Additionally, indoor robots, including service robots and emergency-responne systems, can operate
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more safely and effectively in complex environments around unseen obstacles. However, the increased sensing capability also raises privacy considerations, the technology could potentially detect individuals without their knowledge or consent. Thus, responsible deployment of our system must include transparency about usage and careful consideration of privacy protections balance safety benefits with ethical obligations.
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Reviews:
[{"review_id": "C2ZHnFH46Z", "reviewer": "Reviewer_rCvz", "summary": "The manuscript presents HoloRadar, a method for 3D reconstruction for both Non-Line-of-Sight (NLOS) and Line-of-Sight (LOS) perceptions, using a rotating mmWave radar sensor. The work consists of a preprocessing step and a two-stage pipeline. \n\nFirst, data is collected in cycles by rotating a radar to form a cylindrical array covering the whole surroundings. For signals collected after each rotation, beamforming is applied to obtain a 3D heatmap, consisting of Elevations, Azimuths and Range bins. \n\nThe first stage of the proposed pipeline uses the 3D heat maps as input. In particular, for each Azimuth-Elevation pair, a deep model estimates a set of bounce distances, each corresponding to a reflection along a multi-return path. Through the use of an encoder/multi-decoder architecture, this multi-return imaging model predicts the range images for each bounce corresponding to the original 3D radar heatmap.\n\nThe second stage reconstructs a physical 3D scene through a ray-tracing based learned model an a deep refinement procedure. In particular, the predicted multi-return images are combined with normal estimation at each bounce, to reverse the mirroring effect of bounces. Lastly, residual geometric errors are accounted for by exploiting a voxel-based 3D UNet, which fuses predictions from different bounces and refines the reconstruction with a volumetric representations.\n\nHoloRadar has been deployed on a real-world mobile robot and it has been evaluated across multiple environments, depicting corners, showing promising and accurate results.", "questions": "1. [SUGGESTION] According to what discussed in the weaknesses, a more detailed explanation of the experimental settings could refute the possibility of overfitting and non-adaptability to diverse scenes/structures.\n    2. [QUESTION] Why did the authors use only the F-Score to assess the reconstruction quality? One could have included Chamfer or Hausdorff distances, or Mesh quality (e.g., watertightness, manifoldness, self-intersections).\n    3. [SUGGESTION] The authors should include information about the processing time and requirements of the proposed method.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths.\n    1. The manuscript is well written and detailed, with all the various aspects being fully explained (from the various steps of the processing pipeline, to the description of the training procedure). This clarity allows for replicability (not considering the (un)availability of the dataset).\n    2. The presented pipeline is relatively simple, yet effective, both in terms of adopted learning models, training setup (e.g., loss functions) and mathematical formulation.\n    3. Both evaluation and ablation study show promising results in terms of metrics.\n\nWeaknesses.\n    1. The major concern about this work is related to the dataset gathered and used for the experiments. As far as it can be understood in the manuscript, all corners “captured” belong to the same building / complex, which could introduce a strong learning bias, with possible overfit. A similar discussion can be done on the number of heatmap scans gathered in total (around 1k for each corner), especially if there is no substantial difference in heatmaps associated with the same location (the paper does not give such information).\n    2. The manuscript is lacking a more in-depth analysis on the behavior of the proposed method based on the shape (e.g., T-shaped, oblique, rounded), size of corners (e.g., narrow, wide, medium), and number of perceivable entities (e.g., not just people but trash bins, furniture, or general obstacles).\n    3. There is no information in the manuscript about the processing requirements of the proposed method, i.e., if it is able to run in real-time (higher or equal frequency of data acquisition rate) and on which hardware.", "was_revised": true, "final_justification": "I have increased my score to acknoledge the work done by the authors in their rebuttal.", "rebuttal": "We would like to thank the reviewer for the constructive comments. Below we will detail our responses for what we have done or plan to do addressing the concerns raised.\n## 1. Dataset diversity and risk of overfitting\n### Reviewer’s comments: \n(W1) The major concern about this work is related to the dataset gathered and used for the experiments. As far as it can be understood in the manuscript, all corners “captured” belong to the same building / complex, which could introduce a strong learning bias, with possible overfit. A similar discussion can be done on the number of heatmap scans gathered in total (around 1k for each corner), especially if there is no substantial difference in heatmaps associated with the same location (the paper does not give such information).\n\n(Q1) [SUGGESTION] According to what discussed in the weaknesses, a more detailed explanation of the experimental settings could refute the possibility of overfitting and non-adaptability to diverse scenes/structures.\n### Response: \nWe appreciate this concern and have expanded the dataset to make the evaluation more generalizable. The original submission reported 17 corners from 2 buildings, with 6 and 11 corners respectively. We have now extended this to 32 corners across 5 buildings, with building‑wise counts of 6, 11, 5, 5, and 5. This yields a total of 28k heatmap scans. The corner set now includes 21 T‑shaped, 5 L‑shaped, 5 cross‑shaped, and 1 oblique corner at 45°. Corner widths range from 1.33 m to 4.63 m, with a mean of 2.16 m and a standard deviation of 0.89 m. The five buildings were constructed between 1906 and 1996, with renovations between 1973 and 2017, and they feature different architectural styles. These statistics document substantial diversity in geometry and materials. We will add these details to the revised paper.\nTo address variation across scans of the same corner, our data were collected with both a moving robot and moving humans, so sensor and target locations vary across acquisitions. At millimeter‑wave frequencies, even small location changes (i.e., millimeter level) can alter signal phase, thus leading to different heatmaps when doing beamforming. We will include a short analysis visualizing scan‑to‑scan heatmap diversity within the same corner in the supplementary material.\n\n**Results.** We summarize our model’s performance on the expanded dataset in the tables below. Both the multi-return RF imaging model and the reflection-aware scene reconstruction model exhibit performance that is consistent with that in the original submission. For the multi-return RF imaging model, the average change in per-pixel range error across the three bounces is within 5 cm of the original dataset. For the reflection-aware reconstruction, the F-score varies by 5%, indicating minimal degradation. These results suggest that our method generalizes robustly across buildings, corner geometries, and scene variations. We will incorporate these updated tables into the revised manuscript.\n\n**Table 1. Performance of the multi-return RF imaging model**\n| Method |1st bounce error↓| 2nd bounce error↓|3rd bounce error↓|\n|:---:|:-----:|:---:|:----:|\n|   ViT  |  9.81 cm |  24.30 cm  | 40.61 cm  |\n|   DPT  | 8.87 cm | 23.00 cm  | 39.27 cm  |\n|  Ours  | 7.03 cm | 19.03 cm  | 31.36 cm  |\n\n**Table 2. Results for scene reconstruction**\n|  Method | LOS F-score↑ | LOS Chamfer↓ | LOS Hausdorff↓ | NLOS F-score↑ | NLOS Chamfer↓ | NLOS Hausdorff ↓|\n|:---:|:----:|:----:|:---:|:----:|:-----:|:--:|\n| End-to-end ViT |  72.4%  | 19.8 cm  |  13.1 cm   |  40.4%| 45.4 cm    |   28.5 cm   |\n| End-to-end DPT |  69.8%  | 21.5 cm   |  14.6 cm |  37.0% |    50.3 cm |  31.1 cm  |\n| w/o refinement |  83.8%  | 13.8 cm  | 7.3 cm  | 50.3%  |  43.5 cm    |  29.3 cm  |\n|  w/ refinement |  85.7%  |    12.2 cm |   7.1 cm  |  54.6% |    40.0 cm    |  28.3 cm |\n\n## 2. Behavior across corner types, sizes, and perceivable entities\n### Reviewer’s comments: \n(W2) The manuscript is lacking a more in-depth analysis on the behavior of the proposed method based on the shape (e.g., T-shaped, oblique, rounded), size of corners (e.g., narrow, wide, medium), and number of perceivable entities (e.g., not just people but trash bins, furniture, or general obstacles).\n### Response: \n**Corner types and sizes:** We agree that an evaluation of system performance on different types of corners is important to show the robustness and generalizability of our method. Therefore, we have conducted additional experiments and summarized the evaluation results on various corners with different shapes (T-shaped, cross-shaped, and L-shaped) and sizes (narrow: < 1.5 m, medium: 1.5–3 m, wide: >3 m) in the following Table 3. The results exhibit modest variation across categories, indicating that our method remains consistently effective regardless of corner layout.\n\nWe observe that T-shaped corners yield the best performance, likely due to their larger reflective surfaces, which are more effective for multi-bounce signal propagation. Among the size categories, medium-width corners achieve the lowest reconstruction errors, possibly because narrow corners may introduce stronger multipath interference, while wide corners can reduce signal strength due to increased distance and wider beam dispersion.\n\n**Table 3. Performance on different categories of corners**\n|   |   | NLOS Chamfer (cm)↓|NLOS Hausdorff (cm)↓|NLOS F-score (%)↑|\n|---|---|:---:|:---:|:--:|\n| Shape |   T-Shaped   |  34.1 |  23.9 | 61.9 |\n| | Cross-shaped | 37.8 | 24.9 |  47.8  |\n| |   L-shaped | 45.3 |  32.7 |  52.5 |\n|  Size |Narrow | 39.1 |  27.5 |  57.6 |\n|  | Medium | 33.0 | 21.7 |  57.5 |\n| | Wide | 42.5| 32.8 | 59.2|\n\n**Perceivable entities:** Regarding perceivable entities, our method is formulated as class‑agnostic 3D scene reconstruction around the corner, with humans reported explicitly because (i) they are the primary safety‑critical agents, (ii) their kinematics are dynamic and complex, and (iii) downstream use cases are often human‑centric. All other static entities such as furniture, trash bins, and general obstacles are captured within the reconstructed scene geometry, although we do not assign semantic labels to classify them in the current version. To make this clearer and more informative, we will add:\n\n**(1) Scope clarification.** We will state that our pipeline reconstructs full scene geometry and reports a human category for detection and localization. Static objects appear in the geometry but are not separately classified.\n\n**(2) Qualitative evidence.** A new figure will show the reconstructed geometry, the ground truth geometry, and an RGB image, with the static obstacles highlighted. This figure aims to demonstrate that our system can capture entities other than humans.\n\n**(3) Quantitative evaluation.** To further investigate our system performance on the hidden human target, we have carried out experiments on NLOS human detection and localization. For detection (with 0.5 m threshold), we achieve a precision of 93.8%, recall of 87.9%, and F-score of 90.7%. For localization, the median Euclidean distance error is 13.4 cm and the mean error is 15.9 cm. These results show that the system reliably detects a hidden human and localizes it with good accuracy.\n\n## 3. Processing requirements and real‑time feasibility\n### Reviewer’s comments: \n(W3) There is no information in the manuscript about the processing requirements of the proposed method, i.e., if it is able to run in real-time (higher or equal frequency of data acquisition rate) and on which hardware.\n\n(Q3) [SUGGESTION] The authors should include information about the processing time and requirements of the proposed method.\n### Response: \nWe have added a runtime experiment with per‑stage latency measured on an NVIDIA RTX4070 GPU. The multi‑return RF imaging model takes 38.4 ms per heatmap, the learned ray‑tracing model takes 80 ms, and the refinement model takes 180 ms. The end‑to‑end time per scan is 298 ms, corresponding to 3.35 Hz. Since the radar rotation period is 0.5 s per cycle (2 Hz), it shows that our system supports real‑time operation. We will incorporate them in the paper.\n\n## 4. More metrics beyond F‑score\n### Reviewer’s comments: \n(Q2) [QUESTION] Why did the authors use only the F-Score to assess the reconstruction quality? One could have included Chamfer or Hausdorff distances, or Mesh quality (e.g., watertightness, manifoldness, self-intersections).\n### Response: \nWe agree that an F‑score alone is not sufficient.  To evaluate our method more thoroughly, we have added new metrics, namely the Chamfer distance and the Hausdorff distance, for both LOS and NLOS evaluations. The results have been summarized in Table 2 and Table 3 (presented above). Our method achieves the best performance across these metrics when compared to ViT and DPT baselines, demonstrating its effectiveness. We will include metric definitions and implementation details in the supplementary material for reproducibility.\n\n## Summary\nWe appreciate the reviewer’s thoughtful feedback. These comments have helped us strengthen the paper in three concrete ways: \n- **(1)** We have expanded and diversified the dataset.\n- **(2)** We have deepened the analysis across corner geometries and entities.\n- **(3)** We have added both runtime measurements and additional reconstruction metrics.\n\nWe will integrate these results, tables, and clarifications in the revised manuscript."}, {"review_id": "1ORWe9BzFp", "reviewer": "Reviewer_sNnL", "summary": "To address the problem that the current system (e.g., LiDAR) and methods are difficult to apply in robots, the authors propose a mmWave radar-based method and system for NLOS 3D reconstruction. The proposed method integrates physics-based modeling and adopts multiple supervised terms to ensure the accuracy of the reconstructions. Experiments show that the proposed method has the capacity to effectively separate signals of each bounce and manage to reconstruct accurate 3D scenario information.", "questions": "* Include analyses and results for distinct scenario types. (e.g., outdoor scenarios).\n* Include analyses for different material types and light attenuation levels.\n* Provide a detailed description of the loss function.\n* Include the ablation study referenced in the Weaknesses.", "limitations": "See the weaknesses.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n* The paper is well constructed.\n* The authors adopt self-capturing data to train the network. Although lacking of comparable baseline methods, the authors provides resonable explanations and the results in quantitatively and qualitatively.\n\n### Weaknesses\n\n* The dataset examples provided in the paper seem to solely include indoor scenarios with limited spatial space, which suggests that the proposed method may have restricted practical applicability.\n* The paper assumes that the relay wall behaves as a specular reflector. However, light attenuation might also have a relation to occluder material, so the authors should analyze performance under more severe attenuation conditions.\n* Concerns regarding the loss function: Are the range images from the three decoders supervised jointly or separately?\n* The paper lacks a necessary ablation study. While sparse 3D convolutions reduce the computational cost, the inference time might arise due to the additional cost of computing the rulebook. An alternative method is to downsample features to trade off performance and efficiency. Further theoretical comparisons and explicit FLOPS and parameter counts would further clarify the proposed method’s advantages.", "was_revised": true, "final_justification": "I have decided to raise my score to acknowledge the workload and methodological contributions presented in this paper.", "rebuttal": "We appreciate the reviewer’s thoughtful and constructive feedback. Below, we provide a detailed description of how we have addressed, or intend to address, the points raised.\n# 1. Analyses for distinct scenario types\n### Reviewer’s comments: \n(W1) The dataset examples provided in the paper seem to solely include indoor scenarios with limited spatial space, which suggests that the proposed method may have restricted practical applicability.\n\n(Q1) Include analyses and results for distinct scenario types. (e.g., outdoor scenarios).\n### Response: \n**Why we focus on indoor scenarios:** As noted in the limitations section, our current work focuses on indoor around-the-corner scenarios for three main reasons. **(1)** As an initial step toward NLOS 3D scene reconstruction using mmWave radar, indoor environments provide more controlled conditions with less interruptions, enabling more reliable validation of the full pipeline. **(2)** The radar platform we use has limited transmission power. In wide open outdoor environments, signal returns are often sparse or absent across many beam directions, which makes dense reconstruction particularly challenging without longer integration or larger apertures. **(3)** Most existing radar-based dense 3D imaging efforts [8, 18] have focused on indoor scenes. While some outdoor radar works exist, they are either object-specific rather than general-purpose scene-level reconstruction [11], or only focus on 2D detection and tracking [26, A].\n\n**Challenges for indoor scenarios:** While indoor environments are more controlled in terms of external noise, they are far from trivial. They pose unique challenges due to dense multipath effects and clutter from walls, ceilings, floors, and furniture. These complex propagation conditions demand more robust modeling of signal reflections and occlusions. Additionally, human behavior in indoor settings often includes pausing, turning, or standing still. This near-zero radial velocity undermines the effectiveness of Doppler-based detection that is commonly used in outdoor radar detection work [26, A]. These characteristics make robust static and dynamic NLOS perception in indoor scenes especially relevant and nontrivial. For all these reasons, we chose to establish a strong indoor baseline that demonstrates end-to-end feasibility and practical utility for mobile robot navigation in realistic hallway and room environments.\n\n**Additional Experiments for distinct scenario types:** To address the concern about applicability across distinct scenarios, we have expanded our dataset. It now contains 32 corners across 5 buildings, with a total of 28k heatmap scans. The corners span multiple structures, including 21 T‑shaped, 5 L‑shaped, 5 cross-shaped, and 1 oblique (45°) corners. Corner widths range from 1.33 m to 4.63 m, with a mean of 2.16 m and a standard deviation of 0.89 m. The five buildings were constructed between 1906 and 1996, with renovations between 1973 and 2017, and they feature different architectural styles. On the expanded set, both the multi-return RF imaging model and the reflection-aware reconstruction model maintain performance levels comparable to those reported in the original paper, as shown in Tables 1 and 2 below. For the multi-return RF imaging model, the average change in per-pixel range error across the three bounces is within 5 cm of the original dataset. For the reflection‑aware reconstruction, the change in F‑score is 5% relative to the original table. These additions are intended to reduce the risk of site‑specific bias and to document generalization across buildings and layouts.\n\n**Table 1. Performance of the multi-return RF imaging model (mean error, cm↓)**\n|Method|1st bounce|2nd bounce|3rd bounce|\n|:--:|:--:|:--:|:--:|\n|ViT|9.81|24.30|40.61|\n|DPT|8.87|23.00|39.27|\n|Ours|7.03|19.03|31.36|\n\n**Table 2. Results for scene reconstruction**\n|Method|LOS F-score (%)↑|LOS Chamfer (cm)↓|LOS Hausdorff (cm)↓|NLOS F-score (%)↑|NLOS Chamfer (cm)↓|NLOS Hausdorff (cm)↓|\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n|End-to-end ViT|72.4|19.8|13.1|40.4|45.4|28.5|\n|End-to-end DPT|69.8|21.5|14.6|37.0|50.3|31.1|\n|w/o refinement|83.8|13.8|7.3|50.3|43.5|29.3|\n|w/ refinement|85.7|12.2|7.1|54.6 |40.0|28.3|\n\n### References:\n- [A] Scheiner, Nicolas, et al. \"Seeing around street corners: Non-line-of-sight detection and tracking in-the-wild using doppler radar.\" CVPR. 2020.\n\n# 2. Analyses on relay walls of different attenuation levels\n### Reviewer’s comments: \n(W2) The paper assumes that the relay wall behaves as a specular reflector. However, light attenuation might also have a relation to occluder material, so the authors should analyze performance under more severe attenuation conditions.\n\n(Q2) Include analyses for different material types and light attenuation levels.\n### Response: \nWe appreciate the reviewer’s observation regarding attenuation effects. In practice, the level of attenuation from a relay wall is governed by several interdependent factors, including material composition, surface roughness, wall thickness, and angle of incidence. Due to the complexity and interaction among these properties, it is difficult to isolate a single variable for controlled experimentation. To address this, we instead evaluate system performance across a range of real-world attenuation levels, measured as the power drop between LOS reflections and the corresponding NLOS signal.\n\nTo quantify attenuation, we sample the radar heatmap on the LOS region and subtract the energy observed in the hidden target region. We categorize our test data into three bands based on the attenuation value: low attenuation (<10 dB), medium attenuation (10 to 20 dB), and high attenuation (>20 dB). Table 3 summarizes the reconstruction performance across these categories. As expected, the system performs best in low-attenuation scenarios. However, even under substantial signal loss, it continues to yield low Chamfer and Hausdorff distances and maintains a high F-score. These results demonstrate that our method remains robust and effective even with significant signal attenuation.\n\n**Table 3. Reconstruction performance under different attenuation levels**\n|Attenuation|Chamfer (cm)↓|Hausdorff (cm)↓|F-score (%)↑|\n|:--:|:--:|:--:|:--:|\n|High|39.4|27.1|53.1|\n|Medium|37.2|26.4|55.9|\n|Low|35.1|25.5|66.1|\n\n# 3. Supervision of decoders and loss formulation\n### Reviewer’s comments: \n(W3) Concerns regarding the loss function: Are the range images from the three decoders supervised jointly or separately?\n\n(Q3) Provide a detailed description of the loss function.\n### Response: \nWe thank the reviewer for pointing this out and apologize for the lack of clarity. The three decoder branches in our multi-return imaging model are supervised jointly. Each branch predicts a range image corresponding to one bounce and is trained with an L1 per-pixel range estimation loss. As shown in Equation (1), the total loss is computed as the sum of the individual L1 losses across the three branches, which reflects joint supervision. We will clarify this in the revised manuscript to avoid confusion.\n\n# 4. Ablation study on 3D conv design and efficiency\n### Reviewer’s comments: \n(W4) The paper lacks a necessary ablation study. While sparse 3D convolutions reduce the computational cost, the inference time might arise due to the additional cost of computing the rulebook. An alternative method is to downsample features to trade off performance and efficiency. Further theoretical comparisons and explicit FLOPS and parameter counts would further clarify the proposed method’s advantages.\n\n(Q4) Include the ablation study referenced in the Weaknesses.\n### Response: \nWe appreciate the reviewer highlighting this missing analysis. We have conducted a new ablation study to evaluate the trade-offs between performance and efficiency across different model choices. Specifically, we compare four alternatives: (1) our sparse 3D conv model, (2) a full-resolution dense 3D conv model, (3) a 2x and (4) a 4x downsampled dense 3D conv model.\n\nFor each variant, we report inference time (on an NVIDIA RTX4070 GPU), FLOPS, parameter count and our metrics. Our sparse 3D convolution model achieves faster inference time compared to the full-resolution dense convolution, with accuracy close to it. Although sparse convolution requires an additional rulebook, which can potentially increase inference time, our scene is highly sparse, allowing sparse convolution to reduce the overall inference time. This validates our choice to balance between computational efficiency and reconstruction quality. With sparse convolution, the end-to-end inference time is 298 ms (38.4 ms for multi-return imaging and 80ms for ray-tracing), which corresponds to 3.35 Hz. Given that the radar rotation cycle is 2 Hz, our system can support real-time operation. These results and comparisons will be included in the revised manuscript. \n\n**Table 4. Runtime and performance for model design choices**\n|Design choices|Inference time (ms)|GFLOPS|#Parameters|NLOS Chamfer (cm)↓|NLOS F-score (%)↑|\n|:--:|:--:|:--:|:--:|:--:|:--:|\n|Full-resolution dense 3D conv|274|622.24|54.7 M|37.5|53.7|\n|2x downsampled 3D conv|121|322.33|54.7 M|37.6|47.1|\n|4x downsampled 3D conv|40|247.61|54.7 M|42.7|29.9|\n|Sparse 3D conv (ours)|180| 225.05|53.1 M|40.0|54.6|\n\n# Summary\nWe appreciate the reviewer’s detailed feedback. These comments have helped us improve the paper in four meaningful ways: \n- **(1)** We expanded and diversified the dataset to better assess generalization across different layouts and scene types.\n- **(2)** We conducted new analyses to evaluate performance under varying attenuation levels. \n- **(3)** We clarified the supervision strategy and formulation of the loss function.\n- **(4)** We added an ablation study that compares different 3D conv designs with inference time and performance.\n\nWe will integrate these results, tables, and clarifications into the revised manuscript."}, {"review_id": "nhBmkyzDnb", "reviewer": "Reviewer_sn4N", "summary": "The authors present an approach to find non-line-of-sight (NLOS) 3D objects in indoor scenes by finding the multi-return range images and then reconstructing the physical scene by providing the reflected signals to their \"true\" locations. The HoloRadar architecture takes the RF heatmap as input and predcts the scene around corners. They show that they can recover hidden objects in the indoor environment.", "questions": "Posted in weakness.", "limitations": "The limitations address the indoor aspect of the problem.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 1, "originality": 1, "strengths_and_weaknesses": "Strengths - The paper is well-written and well-explained. \nWeakness - \nThe authors have missed a paper on exactly the same topic which deals with outdoor environments and predicts trajectory of various objects - \nScheiner, N., Kraus, F., Wei, F., Phan, B., Mannan, F., Appenrodt, N., ... & Heide, F. (2020). Seeing around street corners: Non-line-of-sight detection and tracking in-the-wild using doppler radar. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2068-2077).\nIn view of this omission, the paper is weakened sufficiently since this deals with the indoor environment only and does not provide any solution for the outdoor clutter.", "was_revised": true, "final_justification": "Updated score based on rebuttal.", "rebuttal": "We thank the reviewer for raising a concern on related work, which provides an opportunity for us to clarify our contributions.\n# 1. Clarifying relation to prior work\n### Reviewer’s comments: \n(W1) The authors have missed a paper on exactly the same topic which deals with outdoor environments and predicts trajectory of various objects - Scheiner, N., Kraus, F., Wei, F., Phan, B., Mannan, F., Appenrodt, N., ... & Heide, F. (2020). Seeing around street corners: Non-line-of-sight detection and tracking in-the-wild using doppler radar. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2068-2077). In view of this omission, the paper is weakened sufficiently since this deals with the indoor environment only and does not provide any solution for the outdoor clutter.\n\n(L1) The limitations address the indoor aspect of the problem.\n### Response: \nWe thank the reviewer for raising this concern and for pointing out the work by Scheiner et al. (CVPR 2020). We appreciate the relevance of this paper to NLOS radar perception, but we respectfully argue that the goals, assumptions, and technical challenges addressed in our work are substantially different. In fact, our contribution builds on and extends beyond the scope of Scheiner et al., tackling a more general and more difficult task: full-scene 3D reconstruction, including static and dynamic elements, in cluttered and multipath-rich indoor environments using only radar.\n\nBelow, we summarize the key differences and explain why our method represents a complementary advance.\n\n**Task differences:** Scheiner et al. focus on 2D detection and tracking of moving humans, reporting sparse trajectories on a 2D map. In contrast, our method performs dense 3D reconstruction of both environmental structures and human targets, producing high-resolution range images and full 3D point clouds. This leap in dimensionality and fidelity introduces significant algorithmic and modeling complexity.\n\n**Extra sensor assumptions:** Scheiner et al. rely on a LiDAR sensor to detect and localize the relay wall, a dependency that limits robustness in conditions with visual occlusion (e.g., fog or dust). Our system operates entirely with radar, which is inherently robust to airborne occluders. We estimate all reflective surfaces using only RF signals from a rotating mmWave radar, without relying on auxiliary depth sensors.\n\n**Reflection modeling:** The system in Scheiner et al. handles single-bounce reflections from a known wall, applying rule-based methods to map mirrored points back to the scene. This works in simplified cases but breaks down with multi-bounce propagation. Our model introduces a learned multi-return RF imaging module that produces a distinct 2D image for each bounce, effectively simulating a “multi-return LiDAR”. We then reconstruct the full scene using a deep reflection-aware model that handles complex multi-bounce spatial relationships.\n\n**Environmental complexity:** We respectfully disagree that our focus on indoor environments weakens the significance of our contribution. Indoor scenes also present unique challenges for radar-based perception. Doppler-based detection, which Scheiner et al. rely on, performs well in outdoor environments with fast-moving targets (e.g., pedestrians, cyclists). However, in indoor spaces, people often pause, turn, or remain stationary (e.g., during conversations), resulting in near-zero radial velocity that renders Doppler cues ineffective. Furthermore, the presence of dense clutter and reflective surfaces introduces significant multipath, requiring robust signal modeling. In contrast, outdoor settings are typically sparser, with fewer and weaker multipath interactions.\n\n**Technical significance:** To the best of our knowledge, our system is the first to achieve full-scene 3D reconstruction around corners, capturing both LOS and NLOS structures and humans using only radar. Our two-stage pipeline decouples signal interpretation from spatial reasoning and avoids brittle hand-crafted heuristics. Specifically: (1) Our multi-return RF imaging model resolves multiple bounces and produces high-resolution range images per bounce. (2) Our reflection-aware scene reconstruction model reasons over complex radar point relationships to recover full 3D geometry. Together, these components enable dense reconstruction in environments where prior systems, including Scheiner et al., cannot operate reliably.\n\n# Summary\nWhile Scheiner et al. make a valuable contribution to radar-based outdoor NLOS detection, their system focuses on 2D tracking of moving targets under simpler signal conditions. Our work targets a more complex and generalizable problem: full-scene 3D reconstruction, including static geometry and dynamic (or static) agents, in multipath-dense, Doppler-sparse indoor environments. Rather than a simplification or omission, our work should be viewed as a complementary extension into a harder domain, and we hope this clarification helps to clearly distinguish the two contributions."}, {"review_id": "FlwuYG0vi7", "reviewer": "Reviewer_xFjW", "summary": "The paper presents a novel around-the-corner imaging system based on millimeter-wave radar. By employing a series of deep learning models, the system processes radar heatmaps to reconstruct 3D scenes in non-line-of-sight (NLOS) scenarios. The authors collect a custom dataset involving 17 corner configurations and demonstrate that their system can accurately reconstruct scenes, even when objects are occluded from direct view.", "questions": "1. Why were vision-based models (e.g., transformers) used as baselines instead of radar-focused multi-bounce imaging methods, which would provide a more meaningful comparison?\n2. Could the authors provide detailed information about the diversity of corner scenarios (e.g., widths, angles, distances) in their dataset?\n3. Why is the hidden imaging evaluation (human location only) not integrated into the main body of the paper? Would the authors consider moving it and adding clearer evaluation metrics like hit/miss rate?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The work is well-motivated, addressing the important problem of NLOS perception.\n- The overall system architecture is thoughtfully designed, and the inclusion of a ray tracing module contributes a novel component.\n- The dataset collected by the authors adds value and supports meaningful evaluation.\n\nWeaknesses:\n- The related work comparison is limited. References to vision transformers justify architectural choices but do not provide a relevant baseline for evaluating the core novelty of the method. It would be more appropriate to compare the method with other radar-based multi-bounce imaging approaches, which are more relevant to the core technical contribution.\n- The dataset consists of 17 corners, but additional details about the diversity of these scenes—such as the corridor widths and corner angles—would help assess the generalizability of the results under the proposed experimental setup.\n- The evaluation of hidden target imaging (i.e., human location detection without wall depth estimation) should be more prominent. While there is a brief discussion in the supplementary material, this evaluation is crucial and deserves a dedicated section in the main paper, ideally with clear metrics such as hit/miss rate.\n- The paper uses “NLOS” broadly, encompassing both behind-wall and around-the-corner scenarios. However, the method focuses specifically on around-the-corner imaging. This distinction should be emphasized early on, potentially starting from the title.", "was_revised": true, "final_justification": "The rebuttal has addressed most concerns and improved the paper. Although some clarifications on the motivation for full-scene reconstruction and literature context for hidden-human imaging would strengthen it further, I believe the current version shows sufficient novelty and solid evaluation. I am raising my score accordingly and recommend acceptance.", "rebuttal": "We sincerely thank the reviewer for the valuable feedback. In the following, we address each concern with details on our current updates and intended revisions.\n# 1. Missing radar-based multi-bounce imaging baselines\n### Reviewer’s comments: \n(W1) The related work comparison is limited. References to vision transformers justify architectural choices but do not provide a relevant baseline for evaluating the core novelty of the method. It would be more appropriate to compare the method with other radar-based multi-bounce imaging approaches, which are more relevant to the core technical contribution.\n\n(Q1) Why were vision-based models (e.g., transformers) used as baselines instead of radar-focused multi-bounce imaging methods, which would provide a more meaningful comparison?\n### Response:\nWe thank the reviewer for raising this important point. We agree that radar-based methods would be ideal baselines if appropriate counterparts existed. In fact, we implemented and tested the prior method in [8], which performs NLOS around-the-corner imaging using radar by modeling the relay wall as a specular reflector and applying traditional matched filtering. However, it achieved an NLOS imaging F-score of only around 10%, likely due to the absence of a learning component and lack of scene-level reconstruction capability. For this reason, we did not adopt it as a direct baseline, as the comparison would be both unfair and uninformative.\n\nTo further clarify the landscape of related work, we note that existing radar-based NLOS efforts such as [8, A, B] do not support (i) per-pixel 2D range image estimation for NLOS scene geometry, or (ii) dense 3D reconstruction around corners. These two capabilities are central to our contribution and are enabled by our multi-return RF imaging model and reflection-aware reconstruction pipeline.\n\nGiven the lack of radar-based methods that align with the scope and output of our system, we chose to compare against well-established vision-based architectures commonly used for range estimation and scene reconstruction. These models serve as strong and representative backbones, allowing us to assess the effectiveness of our architectural choices in isolation.\n\nWe agree that future work should aim to benchmark against more radar-based systems as the field matures. In the meantime, our comparisons against general-purpose dense reconstruction models help establish the technical validity of our architectural components, while the qualitative and quantitative results support the novelty of our radar-specific contributions.\n\n### References:\n- [A] Li, Yuanji, et al. \"Millimeter-wave non-line-of-sight imaging.\" 2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP). IEEE, 2022.\n- [B] Wei, Shunjun, et al. \"Nonline-of-sight 3-D imaging using millimeter-wave radar.\" IEEE Transactions on Geoscience and Remote Sensing 60 (2021): 1-18.\n\n# 2. Details about the diversity of dataset\n### Reviewer’s comments: \n(W2) The dataset consists of 17 corners, but additional details about the diversity of these scenes—such as the corridor widths and corner angles—would help assess the generalizability of the results under the proposed experimental setup.\n\n(Q2) Could the authors provide detailed information about the diversity of corner scenarios (e.g., widths, angles, distances) in their dataset?\n### Response: \nWe thank the reviewer for highlighting the need for more detailed characterization of our dataset. In response, and as part of our effort to strengthen the experimental validation, we have expanded the dataset from 17 corners across 2 buildings to 32 corners across 5 buildings. This expansion significantly increases the geometric and architectural diversity of the evaluation settings. Detailed information and performance are provided below.\n\n**Corner details:** The five buildings represented in the dataset were constructed between 1906 and 1996, with renovations occurring between 1973 and 2017, capturing a variety of architectural styles and interior layouts. The updated dataset includes 21 T-shaped corners, 5 L-shaped, 5 cross-shaped, and 1 oblique corner with a 45° intersection. Corridor widths span from 1.33 m to 4.63 m, with a mean of 2.16 m and a standard deviation of 0.89 m. This distribution reflects both narrow and wide spaces, contributing to a broad set of scene geometries and occlusion conditions.\n\n**Results:** On the expanded set, both the multi-return RF imaging model and the reflection-aware reconstruction model maintain performance levels comparable to those reported in the original paper, as shown in Tables 1 and 2 below. For the multi-return RF imaging model, the average change in per-pixel range error across the three bounces is within 5 cm of the original dataset. For the reflection‑aware reconstruction, the change in F‑score is 5% relative to the original table. These additions are intended to reduce the risk of site‑specific bias and to document generalization across buildings and layouts.\n\n**Table 1. Performance of the multi-return RF imaging model (mean error, cm↓)**\n| Method | 1st bounce | 2nd bounce | 3rd bounce |\n|:--:|:---:|:---:|:--:|\n|ViT| 9.81|24.30|40.61|\n|DPT| 8.87 |23.00|39.27|\n|Ours| 7.03 |19.03 |31.36|\n\n**Table 2. Results for scene reconstruction**\n|Method|LOS F-score (%)↑|LOS Chamfer (cm)↓|LOS Hausdorff (cm)↓|NLOS F-score (%)↑|NLOS Chamfer (cm)↓|NLOS Hausdorff (cm)↓|\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n|End-to-end ViT|72.4|19.8|13.1|40.4|45.4|28.5|\n|End-to-end DPT|69.8|21.5|14.6|37.0|50.3|31.1|\n|w/o refinement|83.8|13.8|7.3|50.3|43.5|29.3|\n|w/ refinement|85.7|12.2|7.1|54.6 |40.0|28.3|\n\n\n# 3. Evaluation of hidden human imaging\n### Reviewer’s comments: \n(W3) The evaluation of hidden target imaging (i.e., human location detection without wall depth estimation) should be more prominent. While there is a brief discussion in the supplementary material, this evaluation is crucial and deserves a dedicated section in the main paper, ideally with clear metrics such as hit/miss rate.\n\n(Q3) Why is the hidden imaging evaluation (human location only) not integrated into the main body of the paper? Would the authors consider moving it and adding clearer evaluation metrics like hit/miss rate?\n### Response: \nWe agree with the reviewer that evaluating hidden human imaging is critical and deserves more prominent placement in the main paper. In the original submission, this analysis was included in the supplementary material due to space constraints. However, we now recognize that this evaluation is central to many use cases and plan to integrate it directly into the main body of the revised manuscript.\nIn particular, we will present a dedicated evaluation on NLOS human detection and localization, using well-defined and interpretable metrics:\n\n**(1) Detection performance** will be measured using precision, recall, and F1-score (analogous to hit/miss rate), based on whether the predicted human location falls within a 0.5 m threshold of the ground truth.\n\n**(2) Localization accuracy** will be evaluated using the Euclidean distance between predicted and true 3D human positions, reporting both the median and mean error.\n\nFor completeness, we also include these results here. Using a 0.5 m detection threshold, our system achieves a precision of 93.8%, recall of 87.9%, and F1-score of 90.7%. The median Euclidean localization error is 13.4 cm, and the mean error is 15.9 cm. These results demonstrate that our system reliably detects and accurately localizes hidden human targets under NLOS conditions.\n\nWe appreciate the reviewer’s suggestion, which helps improve the clarity, focus, and completeness of our evaluation.\n\n# 4. Clarifying the scope of “NLOS”\n### Reviewer’s comments: \n(W4) The paper uses “NLOS” broadly, encompassing both behind-wall and around-the-corner scenarios. However, the method focuses specifically on around-the-corner imaging. This distinction should be emphasized early on, potentially starting from the title.\n### Response: \nWe thank the reviewer for highlighting this important distinction. We agree that our method is specifically designed for around-the-corner scenarios, which represent a particular subset of the broader non-line-of-sight (NLOS) domain. While the term “NLOS” is commonly used in literature to cover a range of occluded perception problems, including through-wall and behind-obstacle settings, our work is focused on the challenges posed by occlusion due to wall geometry at corners. To make this scope more precise and immediately clear to readers, we will make the following revisions:\n\n**(1) Title update:** We will revise the title to explicitly reflect the focus on around-the-corner imaging and reconstruction.\n\n**(2) Abstract and introduction:** We will update both sections to clearly state that the target application is around-the-corner perception, and distinguish it from other forms of NLOS imaging such as through-wall radar.\n\nWe believe these clarifications will help set appropriate expectations for readers and more accurately position the contribution of our work. We appreciate the reviewer’s suggestion to improve the precision and clarity of the paper.\n\n# Summary\nWe sincerely thank the reviewer for the thoughtful and valuable feedback. These comments have helped strengthen our work in four ways:\n- **(1)** We clarified the novelty of our contributions in the absence of directly comparable radar-based baselines.\n- **(2)** We expanded and better documented the geometric diversity of our dataset.\n- **(3)** We added the hidden human imaging evaluation into the main text with clearer metrics\n- **(4)** We refined the framing of our work to explicitly focus on around-the-corner scenarios within the broader NLOS domain. \n\nWe will incorporate all corresponding revisions into the updated manuscript."}]

Author rebuttal:

Meta-review: All reviewers unanimously agree that this paper makes significant contributions and recommend acceptance (5,5,5,4). After a careful evaluation, the AC concurs with the reviewers’ assessments and also recommends acceptance.
Decision: Accept (poster)