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of autonomous driving and mobile robot research,
underwater 3D reconstruction remains a challenging
research problem. This is because water, unlike air,
has high electrical conductivity and dielectric
constants. Thus, it is not easy to receive GPS signals
underwater. Additionally, the scattering and
absorption properties of light in water differ from
those in air. The presence of suspended particles in
water, originating from human or natural phenomena,
further attenuates light. (Menna et al 2018).
Therefore, it is challenging for cameras to extract
sufficient information for subsequent feature point
detection, matching, camera motion recovery, and
depth and disparity estimation of the 3D scene. The
unique physical properties of water also limit the use
of light detection and ranging system (LiDAR), a fast
and reliable 3D reconstruction method on land,
significantly reducing the effective detection range of
targets. In order to minimize the interference of the
underwater environment, to enhance the real-time
accuracy and robustness of underwater 3D
reconstruction, more effective sensors and data
processing methods are required.
In order to perform 3D reconstruction in water and
other complex media, researchers have developed
single-photon LiDAR imaging sensors in recent
years. Single-photon LiDAR imaging sensor has high
surface delineation capabilities and optical
sensitivity. The systems are typically based on the
Time-of-Flight (ToF) method and Time-Correlated
Single Photon Counting (TCSPC) technology.
Single-photon LiDAR utilizes short pulse widths and
high repetition rate lasers, combined with highly
sensitive detectors, to detect and count returned single
photons. Using TCSPC technology, single-photon
LiDAR is capable of high-resolution 3D single-
photon imaging in scattering underwater
environments (Maccarone et al 2015). Although there
are many advantages using single-photon LiDAR
imaging, capturing enough photon events to establish
accurate parameter estimates may require a long
acquisition time. To further increase the speed of
acquiring scene depth data and simplify the optical
configuration, researchers have developed the Single-
photon avalanche diode (SPAD) for active imaging.
A study by A. Maccarone et al. utilized a
Complementary Metal Oxide Semiconductor
(CMOS) SPAD detector array combined with TCSPC
timing electronic components, reaching visualization
frame rates of 10Hz in scattering environments with
distances up to 6.7AL between the moving target and
the systems (Maccarone et al 2019). However, these
studies did not achieve real-time imaging systems.
One of the main limitations comes from the data
processing segment. The SPAD array's high data rate
had a significant impact on data processing since
larger data volumes require longer processing times
to estimate the intensity and distance data distribution
of targets, which reduces the potential for real-time
reconstruction.
To further enhance the real-time capabilities of
single-photon 3D reconstruction technology,
researchers have developed custom computational
models implemented on Graphics Processing Units
(GPUs). J. Tachella et al. introduced a Real-time 3D
algorithm (RT3D) that uses point cloud denoising
tools presented priviously in a plug-and-play
framework. The RT3D method build fast and robust
distance estimation for single-photon LiDAR
(Tachella et al 2019). RT3D achieved video rates of
50 Hz with processing times as low as 20 ms. K.
Drummond et al. studied the joint surface detection
problem of single-photon LiDAR data, with the and
depth estimation problem, proposing a 3D
reconstruction algorithm based on combined surface
detection and distance estimation (Drummond et al
2021). S. Plosz et al. introduced a highly robust, fast
single-photon LiDAR 3D reconstruction algorithm
and applied it to a pre-collected underwater dataset,
and in the environment up to 4.6AL, they achieved 10
milliseconds processing times (Plosz et al 2023).
However, these studies did not produce a combined
acquisition device and GPU into a comprehensive
underwater 3D reconstruction imaging system.
This paper presents the comprehensive
underwater 3D reconstruction system proposed by A.
Maccarone et al., which is based on the Si-CMOS
SPAD detector array and incorporates real-time
imaging capabilities with a workstation that is GPU-
equipped (Maccarone et al 2023). The discourse
further contrasts the performance of the RT3D
algorithm with the traditional cross-correlation
method and a recently developed method that
amalgamates surface detection and distance
estimation by Drummond et al. within this
experimental framework.
2 METHODS
This section elucidates the fundamental principles of
single-photon LiDAR, accompanied by a concise
overview of traditional cross-correlation method, the
RT3D algorithm and the recently developed
algorithms that amalgamate surface detection with
distance estimation (Ensemble Method). The RT3D
method is restricted to reconstructing one surface per
pixel at most to ensure fairness in comparison. This
Real-Time 3D Reconstruction Based on Single-Photon LiDAR for Underwater Environments
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