# Single-pixel 3D imaging based on fusion temporal data of single photon
detector and millimeter-wave radar

Tingqin Lai  1,2 Xiaolin Liang  1,2 Yi Zhu  3 Xinyi Wu  1,2 Lianye Liao  1,2
Xuelin Yuan  1,2 Ping Su  3 and Shihai Sun1,2  1School of Electronics and
Communication Engineering, Sun Yat-sen University, Shenzhen, Guangdong 518107,
P.R. China.
2Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming
District, Shenzhen, Guangdong 518107, P.R. China
3Tsinghua Shenzhen International Graduate School, Tsinghua University,
Shenzhen, 518055
<EMAIL_ADDRESS>
††journal: opticajournal††articletype: Research Article

Recently, there has been increased attention towards 3D imaging using single-
pixel single-photon detection (also known as temporal data) due to its
potential advantages in terms of cost and power efficiency. However, to
eliminate the symmetry blur in the reconstructed images, a fixed background is
required. This paper proposes a fusion-data-based 3D imaging method that
utilizes a single-pixel single-photon detector and a millimeter-wave radar to
capture temporal histograms of a scene from multiple perspectives.
Subsequently, the 3D information can be reconstructed from the one-dimensional
fusion temporal data by using Artificial Neural Network (ANN). Both the
simulation and experimental results demonstrate that our fusion method
effectively eliminates symmetry blur and improves the quality of the
reconstructed images.

##  1 Introduction

3D imaging finds wide applications in systems such as automatic driving target
recognition, Unmanned Aerial Vehicle (UAV) automatic navigation, and
reconnaissance surveillance. Three main methods are commonly used for 3D
imaging: structured light[1, 2, 3], binocular stereo imaging[4, 5, 6, 7], and
Time-of-Flight (ToF)[8, 9, 10]. ToF imaging has gained significant attention
due to its advantages, including medium and long-distance measurement
capability, high precision, and strong anti-interference ability. Direct Time-
of-Flight (D-ToF) methods estimate depth information by measuring the flight
time of light from the scene to the sensor. Currently, three mainstream
methods are used to capture lateral spatial information in ToF imaging: SPAD
(Single-Photon Avalanche Diode) array sensor[11, 12, 13, 14], single-pixel
imaging combined with specialized structured lighting[15, 16, 17, 18, 19, 20],
and single-pixel imaging combined with scanning techniques[21, 22, 23, 24, 25,
26, 27]. Due to the high cost of SPAD array sensors, the low-cost and highly
sensitive single-pixel imaging method has gained more attention. In recent
years, single-pixel imaging has also produced significant research results.
This method utilizes low-cost color LED arrays for structured illumination,
enabling color imaging with single photodiodes[28]. Moreover, by adding a
small number of photodiodes at different positions, the method can be extended
to three-dimensional imaging. While the above method combines multiple single-
pixel detectors for three-dimensional imaging, it does not achieve true
single-pixel three-dimensional imaging. Ghost imaging employs a computer-
controlled Spatial Light Modulator (SLM) to generate speckle pattern
illumination on the object, eliminating the requirement for beam splitters and
array detectors. Imaging is accomplished by synchronously measuring the light
intensity from the bucket detector[15, 29, 16, 17]. Otherwise, a pulsed laser
uniformly illuminates a Digital Micromirror Device (DMD), used to provide
structured illumination onto a scene, and the back-scattered light is
collected onto a photodiode. The measured light intensities are used in a 3D
reconstruction algorithm to reconstruct both depth and reflectivity images[18,
19, 20]. However, specialized structured lighting is typically not suitable
for long-distance 3D imaging. In contrast, combining single-pixel SPAD with
scanning structures enables long-distance 3D imaging with high spatial
resolution[21, 22, 23, 24]. By implementing time filtering to suppress noise,
single-pixel imaging can achieve an average imaging sensitivity of 0.4 signal
photons per pixel, enabling long-range three-dimensional imaging up to 200
km[25, 26, 27].

To acquire lateral spatial information of targets, the aforementioned single-
pixel imaging methods rely on array sensors, specialized structured lighting,
or single-pixel scanning techniques. However, the high costs and complex
structures associated with these approaches hinder the progress of single-
pixel imaging. Consequently, alternative methods that do not involve scanning
or unique lighting structures have been proposed in recent years[30, 31, 32].
These methods, known as 3D imaging from temporal data, capture the temporal
information (time-of-flight) of the entire scene using a single-pixel Single-
Photon Detector (SPD) and a Time Digital Converter (TDC), followed by
reconstruction of the 3D images using an ANN[33, 34, 35, 36]. However, the
aforementioned methods inherently exhibit symmetry blur issues. This arises
from the fact that a scene with center symmetry, captured by a single-pixel
detector placed at its center, yields identical measurement results. For
instance, the same measurement value is obtained for symmetrical positions on
the left and right sides of a single-pixel sensor. One approach[30] to address
this problem is to introduce a background that reveals the relative position
of the subject. Another strategy[32] involves leveraging multipath time
signals to gather more scene-related data. However, both of these methods
heavily rely on the specific requirements of the scene and may fail to produce
accurate images if the background is a plain wall or lacks distinguishing
features.

This paper proposes a fusion-data-based method for 3D imaging. Our approach
involves placing a single-pixel SPD and a millimeter-wave radar at different
locations, forming a specific angle with respect to the target. The SPD
records the arrival time of return photons from the entire scene as a temporal
histogram, while the millimeter-wave radar captures the one-dimensional range
profile of the scene. The data from the SPD and radar are directly fused and
input into an ANN for 3D scene reconstruction. Millimeter-wave radar is a
radar system that operates in the millimeter-wave frequency band
(approximately 30-300 GHz). This radar technology has high resolution and
accuracy, making it valuable in various applications. In fact, millimeter-wave
radar is widely used in many application, such as autonomous vehicles, drones,
aviation radar, an so on[37, 38, 39]. The fusion approach of SPD + millimeter-
wave radar is used in this paper, because millimeter-wave radar offers
numerous advantages that SPD do not possess. For instance, it has a strong
adaptability to adverse weather conditions and can operate normally in rain,
smog, and heavy snow. Additionally, millimeter-wave radar is less susceptible
to interference from other light sources, such as sunlight or car headlights.
More importantly, millimeter-wave radar is generally cheaper and has a higher
level of integration. Typically, an entire millimeter-wave radar system can be
integrated onto a small circuit board. The added cost of using a system with
SPD + millimeter-wave radar, compared to one using only a single-pixel SPD, is
negligible. Therefore, we believe that the system architecture of SPD +
millimeter-wave radar can fully capitalize on the strengths of both types of
sensors and overcome the weaknesses of using a single sensor, achieving a
synergistic effect where the whole is greater than the sum of its parts.By
integrating the single-pixel SPD and millimeter-wave radar, our method
achieves higher accuracy in 3D scene reconstruction and effectively addresses
the symmetry blur issue without the need for a background. Both simulation and
experimental results demonstrate the successful elimination of symmetry blur
and significant improvement in the quality of reconstructed images.

The remainder of this paper is structured as follows: Section II presents a
theoretical analysis of the imaging method, addressing the associated
challenges, and introduces the imaging algorithm based on ANN. In Section III,
numerical simulations are conducted to compare the performance of the two
imaging methods under various imaging conditions, demonstrating the
effectiveness of the proposed approach. Section IV describes the imaging
experiments performed using an optical system operating at 1550 nm and a 60
GHz millimeter-wave radar, assessing the feasibility of the proposed method.
Section V discusses the potential capabilities of our method in challenging
environments. Finally, Section VI concludes the paper.

##  2 System framework and imaging principle

Figure 1: 3D imaging with fusion data. (a) Data acquisition process. (b) 3D
images recovery process. In 3D images recovery process, the ANN training is
performed only once, then the MLP algorithm can directly reconstructs the 3D
image from the temporal histogram. The Radar represents the millimeter-wave
radar. The human moves in an empty room

The proposed fusion-data-based 3D imaging method is illustrated in Fig.1,
comprising two main processes: data acquisition (Fig.1(a)) and 3D image
recovery (Fig.1(b)). During the data acquisition process, a millimeter-wave
radar is positioned in proximity to a single-pixel SPD at a specific angle
relative to the target under measurement. This arrangement mimics binocular
imaging as the millimeter-wave radar and SPD are separated by a certain
distance. The temporal histogram of the SPD and millimeter-wave data is fused
to generate a fusion histogram by connecting. Concurrently, a high-precision
depth camera captures the 3D image of the target solely for training purposes,
not participating in the image recovery process. By varying the target’s
orientation and position during data acquisition, a substantial amount of real
measured training data is obtained. Each pair of data for 3D image recovery
consists of one fusion histogram and one depth map. In the 3D image recovery
process, the acquired fusion histogram serves as input, while the depth map
functions as output for training an ANN. Sufficient training data and
iterations enable the ANN to effectively learn the mapping between the input
fusion histogram and depth map. Once trained, the network can directly
retrieve new, untrained targets by inputting the acquired fusion histogram
data from the millimeter-wave radar and the SPD.

To describe this method mathematically, it is simple to construct a forward
model where all points in the scene that are at some distance,
$r_{i}=(x_{i},y_{i},z_{i})$, from the SPD provide a related photon arrival
time, $t_{i}=c^{-1}|r_{i}|=c^{-1}\sqrt{(x_{i}^{2}+y_{i}^{2}+z_{i}^{2})}$(where
$c$ is the speed of light). By recording the number of photons arriving at
different times $t_{i}$, we can build up a temporal histogram $H_{s}$ that
contains information about the scene in 3D. This calculation process could be
represented as $H_{s}=F_{s}(I)$, where $I$ represents the model of the 3D
scene and $F_{s}$ represents the calculation from 3D scene to 1D temporal
histogram.

The same purpose as SPD, millimeter wave radar also converts echo collection
containing 3D scene information into a 1D histogram. Assume that the
millimeter wave radar transmits an Linear Frequency Modulation (LFM) pulse,
which is generally in the form of

$s(t)=w(t)exp[j\phi(t)]$ (1)

where $w(t)$ is the real envelop of signal, $\phi(t)$ is the signal modulation
phase, $j$ represents the imaginary unit. For standard LFM signals

$w(t)=rect(\frac{t}{T_{p}}),\phi(t)=2\pi f_{c}t+\pi Kt^{2}$ (2)

where $rect(x)$ denotes the rectangular function, $T_{p}$ is the pulse width
of the signal, $f_{c}$ is the center frequency and $K$ is the chirp-rate. When
the distance of the target is $R$, after the LFM pulse reflected by the
target, the time delay of the echo is $t_{0}=2R/c$, then the echo signal can
be expressed as

$s_{r}(t)=s(t-t_{0})=rect(\frac{t-t_{0}}{T_{p}})exp[j\pi K(t-t_{0})^{2}]$ (3)

To generate the range information of the targets, a matched filter processing
method can be performed. Finally, the output is approximately expressed as

$s_{out}(t)\approx T_{p}sinc[K(t-t_{0})]$ (4)

where $sinc(x)=sin(\pi x)/(\pi x)$. Thus, the target distance information
$s_{out}(t)$, which can be represented as 1D histogram $H_{m}$, can be
extracted from the echo signal. The process of millimeter wave radar obtaining
1D histogram from 3D scene can also be expressed as $H_{m}=F_{m}(I)$. When the
data from SPD and millimeter wave radar are fused, the overall forward model
can be expressed as $H=F(I)$.

The goal is to find a mapping $F^{-1}$ that can recover the 3D image $I$ from
$H$. The specific methods is to adopt a supervised training approach by
gathering a set of temporal histograms corresponding to various scenarios
along with the associated ground-truth 3D images taken with a depth camera.
These data are then used to train the ANN to find an approximate solution to
the $F^{-1}$. When the ANN training is complete (which happens only once), it
can reconstruct new 3D images from the temporal histogram that has not been
trained.

##  3 Numerical results

In this section, we conduct simulations of our proposed fusion-data-based 3D
imaging model and compare it with the method that utilizes only a single pixel
SPD. The specific simulation scenario is depicted in Fig.1. We position
various virtual human models in space and assume that data collection is
performed by the millimeter-wave radar located on the left side of the single-
pixel SPD at a distance of 0.5 m. Meanwhile, the depth camera is positioned in
the center. Simulations are conducted for two cases: with and without
background. In the background-free scenario, we exclude the background and
solely retain the data corresponding to the human model for simulation.
Conversely, in the scenario with background, we situate the human model within
a virtual indoor scene for simulation (further details can be found in the
supplemental document).

To assess the potential performance under ideal conditions, which represent
the best capabilities of current equipment, we conducted simulations with a
system time resolution (impulse response function IFR) set to 2 ps (further
details regarding the analysis of imaging resolution can be found in the
supplemental document). When utilizing only the temporal histogram from a
single-pixel SPD for training, the resulting image after passing through the
ANN closely resembles the one illustrated in Fig.2(a).

Figure 2: Single-pixel single-photon detector imaging simulation result. (a)
Single-pixel 3D imaging symmetry blur result due to lack of background
information, (b) 3D imaging without symmetry blur in the background

Consequently, if a single-pixel SPD alone is employed to detect a target in
the absence of background, the MLP model fails to differentiate between the
left and right sides of the detector, resulting in an image with superimposed
left and right targets. Currently, the prevalent solution for single-photon
detectors involves incorporating a background behind the subject, as depicted
in Fig.2(b). By including asymmetric targets within the background, symmetry
blur is eliminated, and the background imparts relative positional information
during the neural network training of the scene (Turpin et al.[30]). An
alternative approach entails utilizing the multipath effect of radio frequency
or acoustic waves within a confined space to enhance the amount of information
and eliminate symmetry blur (Turpin et al.[32]).

Figure 3: Simulation and reconstruction results of the fusion method. (a)-(c)
Images recovered using a fused data-based 3D image reconstruction algorithm.
(d)-(f) Images recovered using only single photon data. The first column shows
fused temporal histograms generated by simulation [rows (a)–(c)] or histograms
with only single-photon data [rows (d)–(f)], The second column shows the
ground truth depth maps generated by simulation, and the third column shows
the reconstructed images of the time histogram recovered by the MLP algorithm

In real-world scenarios, certain locations such as flat ground, long
corridors, and open rooms may lack sufficiently complex backgrounds and
multipath signals to provide relative positional information in the data. This
gives rise to the inevitable problem of symmetry blur when conducting target
detection in such environments. When the scene is limited and unchangeable,
addressing the challenge of symmetry blur requires improvements in the
detection-end sensor. In this regard, we propose a cost-effective solution
that integrates a millimeter-wave radar sensor and introduces a fusion-data-
based 3D imaging method.

As depicted in Fig.3(a)-(c), the algorithm that fuses millimeter-wave data
successfully eliminates symmetry blur and achieves clear reconstruction of the
human body. Conversely, Fig.3(d)-(f) demonstrate that the single-pixel single-
photon method produces symmetry blur in the absence of background, rendering
it impossible to discern the specific location of the target.

##  4 Experimental results

Numerical simulations were initially conducted to validate the feasibility of
our approach, followed by imaging tests on individuals and objects within the
experimental environment. The experimental system used in the tests is shown
in Fig.4. A supercontinuum laser generated an optical pulse with a width of 6
ps and a wavelength of 1550 ± 25 nm. This pulse was expanded and projected
onto the target through the transmitting optical path of the optical lens
system. The reflected light was collected by the optical lens system and then
coupled into a fiber using a collimator and detected by a Superconducting
Nanowire Single Photon Detection (SNSPD) device via another fiber, with a time
jitter of 50 ps. To generate a temporal histogram, the time-of-flight interval
between the laser and the reflected light was recorded using a Time Digital
Converter (TDC) known as the Swabian Time Tagger Ultra. Simultaneously, the
millimeter-wave radar (model: TI IWR6843) captured the one-dimensional
distance image of the scene, providing a distance resolution of 50 cm and a
range of 0 m to 7 m. Both optical system and millimeter-wave radar focus on
distance measurements, requiring the detection of the flight time of
light/microwaves through space. Due to the inherent delays and errors in the
optical path and electrical circuitry of the system, an object actually
situated 1m away may yield a measured value of 2m or some other distance.
Therefore, distance calibration is necessary. Our calibration method involves
placing a mirror/metal reflector at a distance of 1m from the sensor (aligned
horizontally with the sensor and verified with a high-precision ruler). We
then calibrate the peak value of the detected distance at this position to be
1m. Additionally, the depth camera (model: Orbbec Gemini 2) acquired a three-
dimensional depth map of the scene with a range of 0 m to 6 m. The measured
impulse response function (IRF) of our system was approximately 200 ps.
Specific parameters for each device can be found in the supplemental document.

In the imaging experiment, we conducted tests in an open room to simulate
simple background imaging conditions, as depicted in Fig.4(b). The room had
dimensions of 6 m × 6 m, and the target was positioned at a distance of more
than 3 m to 4 m from the detector. The target’s position within the scene was
randomized. To ensure synchronization, the single-pixel single-photon time-of-
flight measurement system, millimeter-wave radar system, and depth camera were
synchronized every 3 seconds, resulting in the collection of 4000 sets of
fused temporal histograms and 3D images (1000 sets of humans and 3000 sets of
letters). Our method requires less training data and yields high-quality image
reconstruction compared to methods that typically require 6000-10,000 data
sets for training algorithms. The data sets were randomly divided into
training and testing sets in a 9:1 ratio. The imaging results are presented in
Fig.5 (additional experimental results can be found in the supplemental
document).

We evaluate the quality of the reconstructed images by computing the
Structural Similarity Index Measure(SSIM)[40] between the reconstructed images
and the ground truth images, SSIM is a perceptual image quality assessment
method based on human visual characteristics, which can quantify the degree of
distortion in an image and is consistent with human perception of image
distortion. SSIM is calculated by comparing the luminance, contrast and
structural similarity of two images, as shown in Eq.(5)(6)(7)(8)

$l\left(x,y\right)=\frac{2\mu_{x}\mu_{y}+c_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+c_{1}}$
(5)

$c\left(x,y\right)=\frac{2\sigma_{x}\sigma_{y}+c_{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2}}$
(6)

$s\left(x,y\right)=\frac{\sigma_{xy}+c_{3}}{\sigma_{x}\sigma_{y}+c_{3}}$ (7)

$SSIM\left(x,y\right)=\left[l\left(x,y\right)^{\alpha}\cdot
c\left(x,y\right)^{\beta}\cdot s\left(x,y\right)^{\gamma}\right]$ (8)

Among them, $\mu$ is the mean, $\sigma$ is the variance, $sigma_{xy}$ is the
covariance, the constant $c_{n}$ is used to avoid division by 0, the
determination rule is related to the range of pixel values, and the three
power exponents $\alpha$, $\beta$, and $\gamma$ are used to adjust the
importance of the three factors. Generally, the default is 1. At this time,
the calculation formula is:

$SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c_{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2}\right)}$
(9)

Figure 4: Schematic diagram of the experimental system and experimental scene.
(a) Schematic of the layout of the 1550nm fusion-data-based single-photon
single-pixel 3D imaging system which comprises a supercontinuum laser source,
an SNSNP, a TDC module, and Optical lens system, a depth camera, a millimeter-
wave radar, and a laptop. (b) experimental scene with a person. (c) Schematic
diagram of an optical lens system Figure 5: Experiment results. (a)-(d)
Imaging result of different people and objects. In each sub-figure, the
Histogram, the ground-truth depth maps and the retrieved images are shown from
left to right. Moreover, the reconstructed images with fusion data, only
single photon data, only millimeter-wave data are shown from top to bottom

In Fig.5(a)-(d), the first column presents the temporal histograms used for
image reconstruction. The second column displays the ground truth depth maps
captured by a depth camera for comparison. The third column showcases the
reconstructed images based on the histograms from the first column. Each sub-
figure depicts the reconstructed images with fusion data, only single-photon
data, and only millimeter-wave data, arranged from top to bottom. Among the
400 testing data sets, our proposed fusion method exhibits an average
structural similarity index (SSIM) of 0.6576, surpassing the SSIM of the
single-photon method (0.6389) and the radar method (0.5266).

In Fig.5(a) and (b), the fusion method effectively reconstructs clear images
of a person. However, the single-photon-only method lacks background
information, leading to an imprecise determination of the person’s lateral
position and resulting in symmetry blur around the person. The radar results
reveal that the larger IRF causes the loss of certain details in the shapes,
such as incomplete recovery of arms or legs.

In Fig.5(c), imaging experiments were performed with a person holding the
letter "C" (with dimensions of 40 cm × 30 cm). It is evident that the fusion
method achieves the highest imaging quality. The single-photon-only method
displays an artifact on the right side of the reconstructed image, while the
radar method fails to produce a clear image of the letter "C" due to
insufficient resolution.

In Fig.5(d), another imaging experiment was conducted using the letter "T"
(with dimensions of 30 cm × 20 cm). It is evident that when the object being
imaged is smaller, both the photon-only and radar methods yield poor image
reconstruction results, making it challenging to distinguish the person and
the letter. However, when the two sets of data are fused together, the object
can be reconstructed clearly.

The obtained results demonstrate that the fusion-data-driven 3D imaging method
effectively addresses the issue of symmetry blur in single-pixel single-photon
3D imaging. By utilizing a fusion-based approach, the mapping relationship
between 3D imaging and time-of-flight histograms is established more
effectively, leading to enhanced system performance and robustness.

##  5 Discusion

We incorporated millimeter-wave radar as an auxiliary sensing sensor in our
proposed method and experiments to eliminate symmetry blur in imaging and
enhance imaging quality. Millimeter-wave radar was selected due to its
affordability, all-weather operability, and simplicity of data. These
advantages enabled us to achieve considerable improvements in image quality at
a relatively low cost.

In the experiment, adhering to the principles of binocular vision, it was
necessary to maintain a certain separation distance, referred to as the
baseline, between the single-pixel SPD and the millimeter-wave radar. The data
obtained from both sensors consisted of 1D temporal data, eliminating the
requirement for sensor calibration. It is essential to avoid selecting a
baseline that is too close, as this would result in minimal variations in the
collected data when objects are in motion. Due to the limited detection field
of view of the two sensors, an excessively large baseline distance is also
impractical. While a larger baseline enhances imaging accuracy, it introduces
blind spots. The imaging performance associated with different baseline
distances can be evaluated in subsequent studies.

For data fusion, we adopted a straightforward approach by directly
concatenating the 1D single-photon temporal histogram with the millimeter-wave
radar data. This simplified the data processing procedure, avoided increasing
the complexity of the artificial neural network (ANN), and yielded
satisfactory imaging results. More complex and in-depth data fusion methods
can further improve the quality of imaging, which is where future work can be
improved. The combined utilization of the all-weather operation of millimeter-
wave radar and the low-light detection capability of single-photon detectors
endowed our proposed method with excellent imaging potential even in extreme
conditions such as foggy and rainy weather.

In addition to millimeter-wave radar, we employed a 1550 nm laser as the
emission source for the optical system. Compared to the previous work that
used 550 nm, the 1550 nm laser offered improved eye safety and enabled higher
power output. In current autonomous driving vehicles, lidar and millimeter-
wave radar are widely employed independently for detection purposes. However,
our proposed method integrates the raw data from both sensors, resulting in a
robust imaging system. Consequently, our method holds significant potential
for applications in unmanned autonomous navigation platforms and autonomous
driving.

During the ANN training, the ground truth collected by the depth camera
determines the highest resolution of the system. The IRF of the single-photon
system determines the image reconstruction performance of the algorithm. When
the IRF is smaller, the reconstructed image is closer to the ground truth.

##  6 Conclusion

Instead of utilizing structured light illumination or laser scanning, single-
pixel 3D imaging leverages data-driven imaging retrieval algorithms to convert
the 1D temporal histogram obtained from the scene into a 3D depth map.
However, the inherent limitations of single-pixel imaging may give rise to
issues such as symmetry blur in data-driven image retrieval algorithms. In
this paper, we present a novel single-pixel 3D imaging method that integrates
temporal data from a single-photon detector and a millimeter-wave radar.
Specifically, our approach involves capturing temporal histograms of objects
using both a single-pixel single-photon detector and a millimeter-wave radar.
The acquired data are then fused and employed to train a neural network based
on the MLP algorithm, capable of reconstructing 3D images. To validate the
performance of our proposed method, we conducted numerical simulations and
experimental measurements. Remarkably, the results demonstrate the superiority
of our approach over relying solely on a single-pixel single-photon detector,
as it significantly eliminates the impact of symmetrical blur on image
reconstruction. Our proposed method exhibits exceptional imaging performance
and robustness compared to existing approaches. The combination of single
photon detector and millimeter-wave radar enables the development of a novel
3D image reconstruction system, which holds tremendous potential for various
applications, including unmanned autonomous navigation platforms, forward-
looking imaging in vehicles and indoor security monitoring.

## Acknowledgement

This work is supported by Shenzhen Science and Technology Program (Grant No.
JCYJ2022081 8102014029), National Natural Science Foundation of China (Grant
No. 62171458), National key research and development program (Grant No.
2021YFB2802004).

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