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Article
Infrared Laser Speckle Projection-Based Multi-Sensor
Collaborative Human Body Automatic Scanning System
Xiao Yang 1, Juntong Xi 1,2, Jingyu Liu 3and Xiaobo Chen 1,*
Citation: Yang, X.; Xi, J.; Liu, J.; Chen,
X. Infrared Laser Speckle Projection-
Based Multi-Sensor Collaborative
Human Body Automatic Scanning
System. Machines 2021,9, 299.
https://doi.org/10.3390/machines
9110299
Academic Editors: Dan Zhang and
Jinsong Bao
Received: 25 October 2021
Accepted: 21 November 2021
Published: 22 November 2021
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4.0/).
1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
yangxiao1992@sjtu.edu.cn (X.Y.); jtxi@sjtu.edu.cn (J.X.)
2State Key Laboratory of Mechanical System and Vibration, Shanghai 200240, China
3Software Center, Shanghai H VISIONS Technology, Shanghai 200241, China; jyliu@h-visions.com
*Correspondence: xiaoboc@sjtu.edu.cn
Abstract:
Human body scanning is an important means to build a digital 3D model of the human
body, which is the basis for intelligent clothing production, human obesity analysis, and medical
plastic surgery applications, etc. Comparing to commonly used optical scanning technologies such
as laser scanning and fringe structured light, infrared laser speckle projection-based 3D scanning
technology has the advantages of single-shot, simple control, and avoiding light stimulation to
human eyes. In this paper, a multi-sensor collaborative digital human body scanning system based
on near-infrared laser speckle projection is proposed, which occupies less than 2 m
2
and has a
scanning period of about 60 s. Additionally, the system calibration method and control scheme are
proposed for the scanning system, and the serial-parallel computing strategy is developed based on
the unified computing equipment architecture (CUDA), so as to realize the rapid calculation and
automatic registration of local point cloud data. Finally, the effectiveness and time efficiency of the
system are evaluated through anthropometric experiments.
Keywords: laser speckle projection; multi-sensor; pose calibration; human body scanning
1. Introduction
Human body scanning is an important means to build a digital three-dimensional
model of the human body. Intelligent clothing production [
1
], medical plastic surgery [
2
],
3D printing and other fields have wide demands for non-contact human body scanning
based on optical measurement technology [
3
–
6
]. Under the background of intelligent man-
ufacturing, the industrialized tailoring of clothing is booming gradually and transforming
to the goal of intelligence and individuation constantly. The clothing production mode
is to design and make clothing according to the clothing style selected by users and their
body size, which not only meets the requirements of users’ physical characteristics, but
also meets their personalized needs. At present, in addition to the daily clothing sewed
according to the uniform body standard, some clothing needs to be tailored, such as suits,
military uniforms, service uniforms and so on. The traditional custom-made mode mainly
relies on experienced masters to measure the parameters needed for garment making, such
as shoulder width, waist circumference, and arm length, and then cut and sew according to
the measured parameters. Take the customization of a high-class suit as an example, which
goes through complicated and lengthy processes such as size measurement, white embryo
making, white embryo fitting, fabric confirmation, and garment sewing [
7
]. In addition,
the fit and comfort of clothes depends largely on the tailor’s personal experience. In order
to solve the above problems, digital intelligent clothing customization technology based
on garment parameter extraction [
8
] and white embryo production from the digital 3D
model of the user comes into being. Therefore, the complete 3D human body scanning is
the foundation of intelligent garment manufacturing. The point cloud of the human body
can be obtained by 3D scanning, which can quickly and accurately obtain the parameters
Machines 2021,9, 299. https://doi.org/10.3390/machines9110299 https://www.mdpi.com/journal/machines
Machines 2021,9, 299 2 of 13
of several key parts of the garment. At the same time, it can also personalize the parts with
special needs, such as abdomen and back, which can greatly improve the comfort and fit of
clothing to customers.
Commonly used optical 3D scanning techniques mainly include laser triangulation [
9
,
10
]
and structured light [
11
,
12
]. Laser triangulation is a point/line measurement method,
which has the advantages of high measurement accuracy and strong robustness, but the
efficiency is too low for human body scanning. Structured light measurement is a high-
precision area array measurement technology; stripe structured light generally needs
to project multiple coded patterns to obtain complete high-resolution data, during the
process the object needs to keep still. Speckle structured light is based on the principle of
binocular stereo vision, which projects speckle patterns with high randomness and high
contrast on the surface of the object, and then realizes accurate stereo matching by digital
speckle correlation [
13
,
14
]. Therefore, the speckle projection-based 3D scanning technology
is a single-shot 3D measurement technology with high precision. With the continuous
application of intelligent garment technology, single speckle projection measurement
technology has obvious advantages in human body scanning because of its advantages of
dynamic measurement, high precision and full-field measurement.
Due to a limited field of view from a single perspective, it is necessary to register the
data from multiple views of angle to collect complete human body data. The multi-view
3D points registration method can be divided into manual registration and automatic
registration. Manual registration is not suitable for fast automatic scanning because of its
low accuracy and manual intervention. Commonly used automatic registration techniques
include using auxiliary devices such as mechanical structures [
15
] or visual markers [
16
],
and using multi-sensor [
17
–
19
]. For multi-view scanning, the human is required to remain
still during the measurement process, but it is difficult for humans to keep still for a long
time. Therefore, the multi-sensor system is necessary for human body scanning. On
the one hand, multi-sensor can greatly shorten the scanning period. On the other hand,
if single-shot 3D sensors are used, the global single-shot acquisition can be realized by
synchronous sensor controlling, which is especially suitable for human body scanning.
Pesce et al. proposed a low-cost three-dimensional measurement system for 360 degree
single-shot human body scanning with multi-cameras [
20
], which is based on the principle
of binocular stereo vision measurement. During measurement, the human needs to wear
tights with specific coding patterns to assist stereo matching. Leipner et al. introduced
a botscan multi-camera anthropometry system for 3D forensic documentation [
21
]. The
system uses 70 single lens reflex (SLR) cameras, which can collect high-resolution 3D
data in 0.1 s for a human body. The system is based on photogrammetry technology,
and the obtained data have high resolution but the system cost is high. Unlike in the
industrial inspection for quality control, measurement precision is not the main parameter
to consider when choosing a scanner in the field of human scanning. The cost and floor area
of the whole system is more important for such cross-application regions of consumption
and manufacturing.
The premise of automatic data registration is to perform global pose calibration for
multi-sensors. In order to maximize the effective measurement field of view of each sensor,
the common field of view between two multi-sensors is generally very small or even
completely absent. Therefore, the global calibration of multi-sensor without a common
field of view is always the core problem of multi-sensor measurement. Wang et al. realized
the global calibration of multi-sensors by arranging some collinear points or balls with
known distances on a pole, which is equal to the length or width of the global measurement
field of view [
22
]. Kumar et al. proposed a mirror-based global calibration method for
multi-camera network measurement without repeated areas. The essence of this method
is to make the calibration pattern indirectly appear in the field of view of all cameras
through the mirror image [
23
]. Yang et al. put forward the method for global calibration of
multi-camera without a common field of view by using two calibration plates with known
spatial position and orientation relationship [
24
]. This method can achieve high calibration
Machines 2021,9, 299 3 of 13
accuracy, but only if the position and orientation relationship between the two calibration
plates is accurately known, and the calibration flexibility for different systems is low, and
the manufacturing is difficult.
In this paper, we propose a low-cost multi-sensor collaborative human body automatic
scanning system based on single-shot infrared laser speckle projection, which is composed
of three stereo speckle sensors and a precise mechanical turntable. Moreover, an easy-to-
operate system calibration method is proposed to realize global data registration. The
remainder of this paper is organized as follows: The system design and global human body
data collection principle are introduced in Section 2. In Section 3, we present the CUDA-
based parallel computing strategy. Experimental results and discussions are reported in
Section 4. Finally, conclusions are drawn in Section 5.
2. System Design and Global Human Body Data Collection
2.1. System Design
As shown in Figure 1, the designed scanning system consists of a multi-sensor column
and a precision turntable, in which three infrared laser speckle projection-based binocular
sensors are equidistantly distributed on the column. As an aggregate of the regions with
available data (Europe, North America, Australia, and East Asia), statistics show that
95% of the sampled people born between 1980 and 1994 fall in the range from 1632 to
1936 mm [
25
]. In order to be able to scan more than 95% of people in China, taking
the maximum height of 1950 mm as an example, according to the scanning scheme, three
binocular sensors are distributed along the vertical direction, and the effective measurement
height of a single sensor should reach 6500 mm. In order to achieve large scanning scenes at
a possible short working distance, industrial cameras with large target size and short focal
length industrial cameras are preferred on the premise of meeting the scanning precision.
Therefore, half-inch cameras with half-inch target size and lens with 8 mm focal length
are used in the stereo sensor. Based on field of view coincidence constraint, the designed
effective measurement field of view of the stereo sensor is 680 mm
×
544 mm at a working
distance of 880 mm. Three binocular sensors are vertically fixed on the upright column
with equal interval of 650 mm, and the height of the upright column is 2000 mm. The
distance between the axle of turntable and upright post is 1000 mm, and the whole system
covers an area less than 2 m2.
Machines 2021, 9, x FOR PEER REVIEW 3 of 13
camera without a common field of view by using two calibration plates with known spa-
tial position and orientation relationship [24]. This method can achieve high calibration
accuracy, but only if the position and orientation relationship between the two calibration
plates is accurately known, and the calibration flexibility for different systems is low, and
the manufacturing is difficult.
In this paper, we propose a low-cost multi-sensor collaborative human body auto-
matic scanning system based on single-shot infrared laser speckle projection, which is
composed of three stereo speckle sensors and a precise mechanical turntable. Moreover,
an easy-to-operate system calibration method is proposed to realize global data registra-
tion. The remainder of this paper is organized as follows: The system design and global
human body data collection principle are introduced in Section 2. In Section 3, we present
the CUDA-based parallel computing strategy. Experimental results and discussions are
reported in Section 4. Finally, conclusions are drawn in Section 5.
2. System Design and Global Human Body Data Collection
2.1. System Design
As shown in Figure 1, the designed scanning system consists of a multi-sensor col-
umn and a precision turntable, in which three infrared laser speckle projection-based bin-
ocular sensors are equidistantly distributed on the column. As an aggregate of the regions
with available data (Europe, North America, Australia, and East Asia), statistics show that
95% of the sampled people born between 1980 and 1994 fall in the range from 1632 to 1936
mm [25]. In order to be able to scan more than 95% of people in China, taking the maxi-
mum height of 1950 mm as an example, according to the scanning scheme, three binocular
sensors are distributed along the vertical direction, and the effective measurement height
of a single sensor should reach 6500 mm. In order to achieve large scanning scenes at a
possible short working distance, industrial cameras with large target size and short focal
length industrial cameras are preferred on the premise of meeting the scanning precision.
Therefore, half-inch cameras with half-inch target size and lens with 8 mm focal length
are used in the stereo sensor. Based on field of view coincidence constraint, the designed
effective measurement field of view of the stereo sensor is 680 mm × 544 mm at a working
distance of 880 mm. Three binocular sensors are vertically fixed on the upright column
with equal interval of 650 mm, and the height of the upright column is 2000 mm. The
distance between the axle of turntable and upright post is 1000 mm, and the whole system
covers an area less than 2 m2.
Figure 1. Scheme design for human body 3D scanning.
Machines 2021,9, 299 4 of 13
In this scanning scheme, the local data collected by the three binocular sensors on the
column can be transformed into a global coordinate through sensor pose transformation
calibration, and the data collection from head to foot of the human body at one scanning
position can be realized synchronously through camera synchronous control. After im-
age acquisition is completed at this position, the precision turntable carries the human
body to rotate to the next scanning position. The coordinate transformation relationship
between the two scanning positions can be obtained by the rotation angle feedback after
the turntable rotating axle is calibrated. Therefore, the multi-view data of the multi-sensor
and precision turntable scanning scheme can be transformed into a global coordinate,
which can be automatically completed via hardware and software control. In order to
shorten the scanning period as much as possible and consider the degree of vertigo that
the human body can bear caused by the rotation of the turntable, the scheme plans to set
four scanning positions.
2.2. Global Human Body Data Collection
2.2.1. Local Data Registration
There are four scanning positions in a scanning period, and the rotating angle of
the turntable is 90
◦
between every two positions. The starting position of the turntable
is set as the first scanning position, and the rotating angles of 90
◦
, 180
◦
, and 270
◦
are
the second, third, and fourth scanning positions, respectively. As shown in Figure 2,
the three stereo speckle sensors on the column are numbered 1, 2 and 3 from bottom to
top, corresponding to the three measurement coordinate systems, namely
(O1X1Y1Z1)
,
(O2X2Y2Z2)
, and
(O3X3Y3Z3)
, respectively, where the coordinate system of speckle sensor
1 is taken as the global coordinate system of the scanning system.
Machines 2021, 9, x FOR PEER REVIEW 4 of 13
Figure 1. Scheme design for human body 3D scanning.
In this scanning scheme, the local data collected by the three binocular sensors on the
column can be transformed into a global coordinate through sensor pose transformation
calibration, and the data collection from head to foot of the human body at one scanning
position can be realized synchronously through camera synchronous control. After image
acquisition is completed at this position, the precision turntable carries the human body
to rotate to the next scanning position. The coordinate transformation relationship be-
tween the two scanning positions can be obtained by the rotation angle feedback after the
turntable rotating axle is calibrated. Therefore, the multi-view data of the multi-sensor
and precision turntable scanning scheme can be transformed into a global coordinate,
which can be automatically completed via hardware and software control. In order to
shorten the scanning period as much as possible and consider the degree of vertigo that
the human body can bear caused by the rotation of the turntable, the scheme plans to set
four scanning positions.
2.2. Global Human Body Data Collection
2.2.1. Local Data Registration
There are four scanning positions in a scanning period, and the rotating angle of the
turntable is 90° between every two positions. The starting position of the turntable is set
as the first scanning position, and the rotating angles of 90°, 180°, and 270° are the second,
third, and fourth scanning positions, respectively. As shown in Figure 2, the three stereo
speckle sensors on the column are numbered 1, 2 and 3 from bottom to top, corresponding
to the three measurement coordinate systems, namely 1111
()OXYZ , 2222
()OXYZ , and
3333
()OXYZ , respectively, where the coordinate system of speckle sensor 1 is taken as the
global coordinate system of the scanning system.
Figure 2. Schematic diagram of data registration and system calibration.
The system involves two parts of data registration, one is the coordinate transfor-
mation of the point clouds collected by the three sensors on the column. Firstly, the point
clouds collected by sensor 3 and sensor 2 are transformed into the sensor coordinate sys-
tem of sensor 2, and then the transformed point clouds are transformed into the measur-
ing coordinate system of sensor 1 further. The coordinate transformation processes of the
two steps are as follows:
Figure 2. Schematic diagram of data registration and system calibration.
The system involves two parts of data registration, one is the coordinate transforma-
tion of the point clouds collected by the three sensors on the column. Firstly, the point
clouds collected by sensor 3 and sensor 2 are transformed into the sensor coordinate system
of sensor 2, and then the transformed point clouds are transformed into the measuring
coordinate system of sensor 1 further. The coordinate transformation processes of the two
steps are as follows:
x2
y2
z2
1
=MS2
S3
x3
y3
z3
1
(1)
Machines 2021,9, 299 5 of 13
x1
y1
z1
1
=MS1
S2
x2
y2
z2
1
(2)
where
MS2
S3
denotes the pose transformation matrix from
(O3X3Y3Z3)
to
(O2X2Y2Z2)
, and
MS1
S2
denotes the pose transformation matrix from
(O2X2Y2Z2)
to
(O1X1Y1Z1)
.
(x3
,
y3
,
z3)
,
(x2
,
y2
,
z2)
, and
(x1
,
y1
,
z1)
denote the 3D points in the corresponding coordinate system.
The other part is the data registration of the four scanning positions when the turntable
rotates. Taking position 1 as the coordinate datum, data collected in the other three positions
can be transformed into the coordinate system of first position by reversely rotating the
angles of 90
◦
, 180
◦
, and 270
◦
around the turntable axle, respectively. In addition, it is
necessary to obtain the mathematical expression of the turntable axle in the coordinate
system of (O1X1Y1Z1). The registration process around the rotating axle is as follows:
x
y
z
1
=
rodrigues
nx
ny
nz
·θ0
0 1
x1
y1
z1
1
−
Ox
Oy
Oz
1
+
Ox
Oy
Oz
1
(3)
where
(x1
,
y1
,
z1)
denotes the 3D point collected by sensor 1 or that which has been trans-
formed into the coordinate system of sensor 1 from the other two sensors.
(Ox
,
Oy
,
Oz)
is the coordinate of any point on the turntable axle in the coordinate system
(O1X1Y1Z1)
,
(nx
,
ny
,
nz)
is the normal vector of the turntable axle in the coordinate system
(O1X1Y1Z1)
.
The symbol
θ
means the rotating angle of the turntable at corresponding scanning position
and
rodrigues
denotes the Rodrigues transformation.
(x
,
y
,
z)
is the global point coordi-
nate after registration. To sum up the above data registration process, that is, after the
scanning at each position angle is finished, the local data collected by the three sensors at
each scanning position are firstly transformed into the coordinate system
(O1X1Y1Z1)
by
Equations (1) and (2). And then the transformed data in
(O1X1Y1Z1)
are reversely rotated
around the turntable axle by Equation (3) to realize rotary registration, thus realizing the
data registration of the whole system.
2.2.2. System Calibration
As shown in the right part of Figure 2, system calibration includes three parts. First,
stereo calibration is required for each speckle sensor. Then, the pose transformation
between each two sensors needs to be calibrated. Finally, the mathematical expression
of the turntable axle in the coordinate system of sensor 1 needs to be calibrated. The
field of view of the designed speckle sensor is 680 mm
×
544 mm, a 70-inch LCD screen
(LCD-70SU685A) is used to display the circular spot array image designed according
to the resolution of the display in full screen, and the pixel size of the displayed image
can be calculated from the physical size of the display. Then, Zhang’s plane calibration
algorithm [
26
] is adopted for stereo calibration. Next, it is necessary to calibrate the pose
transformation matrix between the two adjacent sensors because the overlapping field of
view between the two adjacent sensors is very small, which cannot be directly used for
pose calibration. A portable hand-held calibration rod is designed, two calibration target
blocks are connected with a rigid rod to indirectly establish the common field of view
between the two adjacent sensors. As shown in Figure 3a, there is a target block at each end
of the designed calibration rod. The length of the connecting rod is the same as the space
distance between the two sensors. During the calibration process, the target blocks at both
ends should be in the respective fields of view of the two sensors. The two target blocks are
composed of two kinds of circular markers with different diameters, marker identification
method can refer to our previous work [
13
]. It should be noted that the only difference
between the two target blocks is whether there is a large circular marker in the center, so
the two target blocks can be automatically distinguished during calibration. As shown
Machines 2021,9, 299 6 of 13
in Figure 3b, the calibration target coordinate system
(O1X1Y1Z1)
needs to be created,
CREAFORM C-Track
™
| Elite binocular tracking equipment is used to obtain the 3D
physical coordinates of each circular marker.
OSiXSiYSiZSi
and
OSjXSjYSjZSj
are the
coordinate systems of the two adjacent sensors, the posture transformation matrixes from
the calibration target coordinate system to the two sensor coordinate systems are denoted
as
MSi
B
and
MSj
B
. During the process of pose calibration, the 3D coordinates of circular
markers on each target block are measured by the speckle sensor, and then
MSi
B
and
MSj
B
can be calculated according to the corresponding relationship between the measured points
in the sensor coordinate and physical points in the target coordinate system. Therefore, the
pose transformation matrixes from
OSjXSjYSjZSj
to
OSiXSiYSiZSi
is
MSj
B·MSi
B−1
. In
order to improve the calibration accuracy, the attitude of the target bar is adjusted several
times under the precondition that the field of view allows to collect unambiguous images.
The physical distance between multiple target points on the target block and different
target points on another target block is used to establish the optimization error function,
which is then optimized by the Levenberg–Marquardt algorithm [
27
] to obtain optimal
pose transformation matrix.
Machines 2021, 9, x FOR PEER REVIEW 6 of 13
stereo calibration. Next, it is necessary to calibrate the pose transformation matrix between
the two adjacent sensors because the overlapping field of view between the two adjacent
sensors is very small, which cannot be directly used for pose calibration. A portable hand-
held calibration rod is designed, two calibration target blocks are connected with a rigid
rod to indirectly establish the common field of view between the two adjacent sensors. As
shown in Figure 3a, there is a target block at each end of the designed calibration rod. The
length of the connecting rod is the same as the space distance between the two sensors.
During the calibration process, the target blocks at both ends should be in the respective
fields of view of the two sensors. The two target blocks are composed of two kinds of
circular markers with different diameters, marker identification method can refer to our
previous work [13]. It should be noted that the only difference between the two target
blocks is whether there is a large circular marker in the center, so the two target blocks
can be automatically distinguished during calibration. As shown in Figure 3b, the calibra-
tion target coordinate system 1111
()OXYZ needs to be created, CREAFORM C-Track™ |
Elite binocular tracking equipment is used to obtain the 3D physical coordinates of each
circular marker.
()
iiii
SSSS
OX YZ and
()
jjjj
SSSS
OX YZ are the coordinate systems of the
two adjacent sensors, the posture transformation matrixes from the calibration target co-
ordinate system to the two sensor coordinate systems are denoted as i
S
B
M and
j
S
B
M.
During the process of pose calibration, the 3D coordinates of circular markers on each
target block are measured by the speckle sensor, and then i
S
B
M and j
S
B
M can be calcu-
lated according to the corresponding relationship between the measured points in the
sensor coordinate and physical points in the target coordinate system. Therefore, the pose
transformation matrixes from
()
jjjj
SSSS
OX YZ to
()
iiii
SSSS
OX YZ is
()
1
ji
SS
BB
−
⋅MM . In or-
der to improve the calibration accuracy, the attitude of the target bar is adjusted several
times under the precondition that the field of view allows to collect unambiguous images.
The physical distance between multiple target points on the target block and different
target points on another target block is used to establish the optimization error function,
which is then optimized by the Levenberg–Marquardt algorithm [27] to obtain optimal
pose transformation matrix.
(a) (b)
Figure 3. Pose transformation calibration for two adjacent sensors: (a) handheld target calibration rod; (b) calibration dia-
gram.
Finally, it is necessary to calibrate the position of the rotating axle in the coordinate
system of sensor 1. The plane rotation measurement calibration method proposed by Ye
et al. [15] is adopted, the prerequisite of the method is that the calibration target plane is
perpendicular to the plane where the turntable is located. Due to the high machining and
Figure 3.
Pose transformation calibration for two adjacent sensors: (
a
) handheld target calibration rod; (
b
) calibration
diagram.
Finally, it is necessary to calibrate the position of the rotating axle in the coordinate
system of sensor 1. The plane rotation measurement calibration method proposed by
Ye et al. [
15
] is adopted, the prerequisite of the method is that the calibration target plane
is perpendicular to the plane where the turntable is located. Due to the high machining
and assembly accuracy of the turntable, as shown in Figure 4a, a high-precision ceramic
cuboid is vertically placed on the turntable, the target plane can be regarded as a vertical
relationship with the turntable plane, and the turntable is controlled to rotate for multiple
angles, at each angle, sensor 1 is used to measure the surface of the plane. To determine
the rotation axis, we need to determine two factors: direction vector of rotation axis and
turntable center position. To calculate the direction vector of rotation axis, the measured
data of the precise ceramic plate at each position are fitted into the plane firstly, and
then we can obtain multiple fitted planes as shown in Figure 4b. Secondly, compute the
intersection of any two fitted planes, and then calculate the average of the normal vectors
of all the intersection lines, which is regarded as the normal vector of the rotating axle.
In order to solve the turntable center point, we have calculated the distance from any
point on the rotating axle to each fitted plane, which should be constant. Therefore, the
point on the axle can be solved by minimizing the of sum of the distance squares by
the Levenberg–Marquardt algorithm [
27
]. Up to now, the whole measurement system
calibration is finished.
Machines 2021,9, 299 7 of 13
Machines 2021, 9, x FOR PEER REVIEW 7 of 13
assembly accuracy of the turntable, as shown in Figure 4a, a high-precision ceramic cu-
boid is vertically placed on the turntable, the target plane can be regarded as a vertical
relationship with the turntable plane, and the turntable is controlled to rotate for multiple
angles, at each angle, sensor 1 is used to measure the surface of the plane. To determine
the rotation axis, we need to determine two factors: direction vector of rotation axis and
turntable center position. To calculate the direction vector of rotation axis, the measured
data of the precise ceramic plate at each position are fitted into the plane firstly, and then
we can obtain multiple fitted planes as shown in Figure 4b. Secondly, compute the inter-
section of any two fitted planes, and then calculate the average of the normal vectors of
all the intersection lines, which is regarded as the normal vector of the rotating axle. In
order to solve the turntable center point, we have calculated the distance from any point
on the rotating axle to each fitted plane, which should be constant. Therefore, the point on
the axle can be solved by minimizing the of sum of the distance squares by the Levenberg–
Marquardt algorithm [27]. Up to now, the whole measurement system calibration is fin-
ished.
(a) (b)
Figure 4. Turntable axle calibration: (a) schematic diagram; (b) measurement result of rotary plane.
3. CUDA-Based Parallel Computing Strategy
In order to shorten the calculation period, a serial-parallel computing strategy of
speckle image matching using digital image correlation is introduced in this section. Ac-
cording to the CUDA programming specification, the serial-parallel computing strategy
shown in Figure 5 is designed to improve the efficiency of data calculation. Firstly, the left
(reference) and right (target) images captured by binocular sensors are rectified on the
CPU, then the corresponding global memory is created on the GPU, and the rectified im-
ages are uploaded to the designated memory on the GPU. In CUDA, the texture memory
has a faster reading and writing speed than the global memory, and the rectified image
pair and corresponding gradient image need to be read frequently in the matching calcu-
lation process. Therefore, texture memory has the same memory size as the global
memory created on the GPU, and the global memory is banded to the texture memory.
Then, the CPU sends out calculation instructions, which are mapped to the GPU through
kernel function to complete the calculation processes such as establishing related global
lookup table, seed points matching, seed points propagation, 3D reconstruction, and co-
ordinate transformation. Among the above processes, the kernel function can reasonably
set the dimensions of grid and block according to the calculation requirements. The data
are downloaded to the CPU when the calculations are finished, and one group of local
point cloud calculation is completed. As mentioned above, local point cloud computing
Figure 4. Turntable axle calibration: (a) schematic diagram; (b) measurement result of rotary plane.
3. CUDA-Based Parallel Computing Strategy
In order to shorten the calculation period, a serial-parallel computing strategy of
speckle image matching using digital image correlation is introduced in this section. Ac-
cording to the CUDA programming specification, the serial-parallel computing strategy
shown in Figure 5is designed to improve the efficiency of data calculation. Firstly, the left
(reference) and right (target) images captured by binocular sensors are rectified on the CPU,
then the corresponding global memory is created on the GPU, and the rectified images
are uploaded to the designated memory on the GPU. In CUDA, the texture memory has
a faster reading and writing speed than the global memory, and the rectified image pair
and corresponding gradient image need to be read frequently in the matching calculation
process. Therefore, texture memory has the same memory size as the global memory
created on the GPU, and the global memory is banded to the texture memory. Then, the
CPU sends out calculation instructions, which are mapped to the GPU through kernel
function to complete the calculation processes such as establishing related global lookup
table, seed points matching, seed points propagation, 3D reconstruction, and coordinate
transformation. Among the above processes, the kernel function can reasonably set the
dimensions of grid and block according to the calculation requirements. The data are
downloaded to the CPU when the calculations are finished, and one group of local point
cloud calculation is completed. As mentioned above, local point cloud computing needs
to be performed 12 times in the whole measurement period, that is, the above-mentioned
serial-parallel process needs to be performed for 12 times during the scanning process.
Machines 2021, 9, x FOR PEER REVIEW 8 of 13
needs to be performed 12 times in the whole measurement period, that is, the above-men-
tioned serial-parallel process needs to be performed for 12 times during the scanning pro-
cess.
Figure 5. Schematic diagram of CUDA-based serial-parallel computing strategy.
4. Experiment and Discussion
The hardware integrated multi-sensor collaborative human body scanning system is
shown in Figure 6. As introduced in Section 2.1, the height of the upright column is 2000
mm, and the distance between the center of the turntable and the upright post is 1000 mm.
The whole system covers an area less than 2 m2. The hardware cost of the measuring col-
umn that is composed of three sensors is about USD 2500, and the hardware cost of the
turntable is about USD 1000. Therefore, the hardware cost of the proposed system is about
USD 3500. In all, the proposed system is low-cost considering the area occupation cost
and hardware cost comprehensively.
After power supply, the system only needs to wait to receive the beginning signal
sent from a personal computer through a network cable, after that it can work automati-
cally. The controlling sequence diagram is shown in Figure 7. The initial position of the
turntable is the first scanning position. At this time, the triggering signal is sent to turn on
the speckle projection module. Then six cameras in the three sensors perform synchronous
hard trigger acquisitions after the three projectors work for 1 ms. When the cameras finish
exposure for 1 ms, the acquisition end signal is sent to the control core panel, thus con-
trolling the projection module to break the circuit. At this time, the first scanning process
ends. When the turntable moves to the next scanning position, the above control proce-
dure repeats. After the last scanning process finished, the control core board feeds back
the ending signal to the PC. At this point, the serial-parallel computing programs are ex-
ecuted, and the turntable rotates back to the initial position.
Figure 5. Schematic diagram of CUDA-based serial-parallel computing strategy.
Machines 2021,9, 299 8 of 13
4. Experiment and Discussion
The hardware integrated multi-sensor collaborative human body scanning system
is shown in Figure 6. As introduced in Section 2.1, the height of the upright column is
2000 mm
, and the distance between the center of the turntable and the upright post is
1000 mm
. The whole system covers an area less than 2 m
2
. The hardware cost of the
measuring column that is composed of three sensors is about USD 2500, and the hardware
cost of the turntable is about USD 1000. Therefore, the hardware cost of the proposed
system is about USD 3500. In all, the proposed system is low-cost considering the area
occupation cost and hardware cost comprehensively.
Machines 2021, 9, x FOR PEER REVIEW 9 of 13
Figure 6. Multi-sensor collaborative human body scanning system.
Figure 7. Controlling sequence diagram of the scanning system.
4.1. Precision Evaluation of the Stereo Sensor
In order to evaluate the measurement precision of the designed stereo sensor, as
shown in Figure 8, a ceramic step block with machining tolerance of ±0.05 mm is measured
by the speckle stereo sensor. The captured stereo images are shown in Figure 8a,b. As
Figure 8c shows, two standard planes marked in Figure 8a are fitted from the recon-
structed point cloud. The fitting standard deviations of plane 1 and plane 2 are 0.137 mm
and 0.097 mm, respectively. Then, nominal depth from plane 2 to plane 1 is 20 mm. Then,
ten reconstructed points are randomly selected on plane 2, the distance from the selected
point to plane 1 is calculated, which is regarded as the measured depth from plane 2 to
plane 2. The statistics of depth measurement error are listed in Table 1, the minimum er-
ror, maximum error, mean error are 0.136 mm, 0.278 mm, and 0.191 mm, respectively. The
above results show that the measurement precision of stereo sensor is higher than 0.3 mm.
Table 1. Statistics for depth measurement error of the ceramic step block.
Point index 1 2 3 4 5 6 7 8 9 10
Depth error 1 0.165 0.136 0.171 0.150 0.251 0.278 0.180 0.155 0.259 0.161
1 Unit: mm.
Figure 6. Multi-sensor collaborative human body scanning system.
After power supply, the system only needs to wait to receive the beginning signal sent
from a personal computer through a network cable, after that it can work automatically.
The controlling sequence diagram is shown in Figure 7. The initial position of the turntable
is the first scanning position. At this time, the triggering signal is sent to turn on the speckle
projection module. Then six cameras in the three sensors perform synchronous hard trigger
acquisitions after the three projectors work for 1 ms. When the cameras finish exposure
for 1 ms, the acquisition end signal is sent to the control core panel, thus controlling the
projection module to break the circuit. At this time, the first scanning process ends. When
the turntable moves to the next scanning position, the above control procedure repeats.
After the last scanning process finished, the control core board feeds back the ending signal
to the PC. At this point, the serial-parallel computing programs are executed, and the
turntable rotates back to the initial position.
4.1. Precision Evaluation of the Stereo Sensor
In order to evaluate the measurement precision of the designed stereo sensor, as
shown in Figure 8, a ceramic step block with machining tolerance of
±
0.05 mm is measured
by the speckle stereo sensor. The captured stereo images are shown in Figure 8a,b. As
Figure 8c shows, two standard planes marked in Figure 8a are fitted from the reconstructed
point cloud. The fitting standard deviations of plane 1 and plane 2 are 0.137 mm and
0.097 mm, respectively. Then, nominal depth from plane 2 to plane 1 is 20 mm. Then,
ten reconstructed points are randomly selected on plane 2, the distance from the selected
point to plane 1 is calculated, which is regarded as the measured depth from plane 2 to
Machines 2021,9, 299 9 of 13
plane 2. The statistics of depth measurement error are listed in Table 1, the minimum error,
maximum error, mean error are 0.136 mm, 0.278 mm, and 0.191 mm, respectively. The
above results show that the measurement precision of stereo sensor is higher than 0.3 mm.
Machines 2021, 9, x FOR PEER REVIEW 9 of 13
Figure 6. Multi-sensor collaborative human body scanning system.
Figure 7. Controlling sequence diagram of the scanning system.
4.1. Precision Evaluation of the Stereo Sensor
In order to evaluate the measurement precision of the designed stereo sensor, as
shown in Figure 8, a ceramic step block with machining tolerance of ±0.05 mm is measured
by the speckle stereo sensor. The captured stereo images are shown in Figure 8a,b. As
Figure 8c shows, two standard planes marked in Figure 8a are fitted from the recon-
structed point cloud. The fitting standard deviations of plane 1 and plane 2 are 0.137 mm
and 0.097 mm, respectively. Then, nominal depth from plane 2 to plane 1 is 20 mm. Then,
ten reconstructed points are randomly selected on plane 2, the distance from the selected
point to plane 1 is calculated, which is regarded as the measured depth from plane 2 to
plane 2. The statistics of depth measurement error are listed in Table 1, the minimum er-
ror, maximum error, mean error are 0.136 mm, 0.278 mm, and 0.191 mm, respectively. The
above results show that the measurement precision of stereo sensor is higher than 0.3 mm.
Table 1. Statistics for depth measurement error of the ceramic step block.
Point index 1 2 3 4 5 6 7 8 9 10
Depth error 1 0.165 0.136 0.171 0.150 0.251 0.278 0.180 0.155 0.259 0.161
1 Unit: mm.
Figure 7. Controlling sequence diagram of the scanning system.
Machines 2021, 9, x FOR PEER REVIEW 10 of 13
(a) (b) (c)
Figure 8. Measurement of a ceramic step block: (a) left image; (b) right image; (c) reconstructed point cloud.
4.2. Precision and Efficiency Evaluation of the Scanning System
Before conducting the scanning experiments, the system is calibrated by the calibra-
tion method described above, then the effectiveness of the proposed scanning system is
verified by three groups of anthropometric model and real human body scanning experi-
ments, the scanning results are shown in Figure 9. For the convenience of introduction,
the three groups from top to bottom in Figure 9 are indexed as I, II, and III. Index I repre-
sents the scanning result of an anthropometric model, II and III represent the scanning
results of two persons. Figure 9a shows the global point clouds coordinate registration of
the local point clouds obtained from 12 local views, in which different colors are used to
distinguish the local point clouds. In order to suppress the registration error, the global
registration function in Geomagic Studio 2012 is used to optimize the registration, the re-
sults are shown in Figure 9b. The optimized point cloud after registration is triangulated
into a mesh model using the “Wrap” function of Geomagic Studio 2012, and then the con-
vex regions and the holes are removed and filled using the “Polygons” module. Figure 9c
shows the modeling renderings after point cloud encapsulation and hole repair. In Figure
9, it can be seen that the proposed system can collect the human body almost completely
except for a small part of data missing in the scanning blind area and local occlusion. The
average distance deviations and standard deviations of the global point clouds optimized
by the function global registration function in Geomagic Studio 2012 are listed in Table 2,
in which the maximum value of the average distance deviation is 1.494 mm and the max-
imum value of the standard deviation is 1.130 mm. The results show that the comprehen-
sive error of point cloud stitching is less than 1.5 mm, and the measurement precision of
the speckle sensor is higher than 0.3 mm. Therefore, the comprehensive scanning error of
the proposed system is less than 2 mm.
Table 2. Statistics for average distance deviations and standard deviations of global point clouds
after optimization of registration.
I II III
Average distance deviation 1 1.154 1.451 1.494
Standard deviation 1 1.026 0.937 1.130
1 Unit: mm.
In order to further evaluate the scanning efficiency, the scanning time costs of the
above three groups of experiments are listed in Table 3. During the working process, when
the turntable rotates from the initial position at 0 degree to the last position at 270 degrees,
the images of all views of angle are collected, and the time of turntable rotation is 30 s.
The image acquisition time is in the millisecond level, which is negligible. The 3D recon-
struction and data registration time of point clouds in the three groups of measurement
fluctuates between 31 s and 33 s, and the total time fluctuates between 61 s and 63 s. From
the above results, it can be seen that the proposed system can complete the whole scanning
Figure 8. Measurement of a ceramic step block: (a) left image; (b) right image; (c) reconstructed point cloud.
Table 1. Statistics for depth measurement error of the ceramic step block.
Point index 1 2 3 4 5 6 7 8 9 10
Depth error 1
0.165 0.136 0.171 0.150 0.251 0.278 0.180 0.155 0.259 0.161
1Unit: mm.
4.2. Precision and Efficiency Evaluation of the Scanning System
Before conducting the scanning experiments, the system is calibrated by the calibration
method described above, then the effectiveness of the proposed scanning system is verified
by three groups of anthropometric model and real human body scanning experiments,
the scanning results are shown in Figure 9. For the convenience of introduction, the three
groups from top to bottom in Figure 9are indexed as I, II, and III. Index I represents the
scanning result of an anthropometric model, II and III represent the scanning results of
two persons. Figure 9a shows the global point clouds coordinate registration of the local
point clouds obtained from 12 local views, in which different colors are used to distinguish
the local point clouds. In order to suppress the registration error, the global registration
function in Geomagic Studio 2012 is used to optimize the registration, the results are shown
in Figure 9b. The optimized point cloud after registration is triangulated into a mesh model
using the “Wrap” function of Geomagic Studio 2012, and then the convex regions and the
holes are removed and filled using the “Polygons” module. Figure 9c shows the modeling
renderings after point cloud encapsulation and hole repair. In Figure 9, it can be seen that
the proposed system can collect the human body almost completely except for a small
part of data missing in the scanning blind area and local occlusion. The average distance
deviations and standard deviations of the global point clouds optimized by the function
Machines 2021,9, 299 10 of 13
global registration function in Geomagic Studio 2012 are listed in Table 2, in which the
maximum value of the average distance deviation is 1.494 mm and the maximum value
of the standard deviation is 1.130 mm. The results show that the comprehensive error of
point cloud stitching is less than 1.5 mm, and the measurement precision of the speckle
sensor is higher than 0.3 mm. Therefore, the comprehensive scanning error of the proposed
system is less than 2 mm.
Machines 2021, 9, x FOR PEER REVIEW 11 of 13
process in about 60 s, during which the human needs to keep as still as possible during
the first 30 s when the turntable rotates.
(a) (b) (c)
Figure 9. Human body scanning results: (a) point clouds of registration by system calibration; (b)
point clouds after registration optimization; (c) 3D model.
Table 3. Statistics for scanning time cost.
Time of
Turntable Rota-
tion 1
Time of
3D Reconstruction and Registra-
tion 1
Total Time 1 Point Number
I 30 32.74 62.74 628939
II 30 31.33 61.33 567893
III 30 31.65 61.65 614126
1 Unit: s.
Figure 9.
Human body scanning results: (
a
) point clouds of registration by system calibration;
(b) point clouds after registration optimization; (c) 3D model.
Machines 2021,9, 299 11 of 13
Table 2.
Statistics for average distance deviations and standard deviations of global point clouds
after optimization of registration.
I II III
Average distance deviation 11.154 1.451 1.494
Standard deviation 11.026 0.937 1.130
1Unit: mm.
In order to further evaluate the scanning efficiency, the scanning time costs of the
above three groups of experiments are listed in Table 3. During the working process, when
the turntable rotates from the initial position at 0 degree to the last position at 270 degrees,
the images of all views of angle are collected, and the time of turntable rotation is 30 s. The
image acquisition time is in the millisecond level, which is negligible. The 3D reconstruction
and data registration time of point clouds in the three groups of measurement fluctuates
between 31 s and 33 s, and the total time fluctuates between 61 s and 63 s. From the above
results, it can be seen that the proposed system can complete the whole scanning process
in about 60 s, during which the human needs to keep as still as possible during the first
30 s when the turntable rotates.
Table 3. Statistics for scanning time cost.
Time of Turntable
Rotation 1
Time of
3D Reconstruction and
Registration 1
Total Time 1Point Number
I 30 32.74 62.74 628,939
II 30 31.33 61.33 567,893
III 30 31.65 61.65 614,126
1Unit: s.
To sum up, the scanning system proposed in this paper can quickly collect dense
3D point clouds of the human body. Comparing to the anthropometric system taking
images with multiple SLR cameras, it has obvious cost advantages. Additionally, near-
infrared speckle measurement can effectively prevent the human eye from being stimulated
by strong light and has the advantage of simple control with only a single acquisition.
The scanning system only needs to receive the signal to start the measurement sent from
software, and then it can automatically realize image acquisition, point cloud reconstruction,
and point cloud registration. The global point cloud can be stored in various data formats
according to the requirements of different fields such as clothing customization, medical
assistance, and human body reminder analysis, thus facilitating different point cloud
processing and modeling software to process specific requirements.
5. Conclusions
Human body 3D scanning is important for many consumption and industry fields,
such as personalized intelligent customization of clothing, human body database establish-
ment, and virtual fitting system development. In this paper, a multi-sensor digital human
body scanning system based on near-infrared laser speckle projection is developed, which
occupies less than 2 m
2
. In order to realize automatic and rapid calculation and registration
of human body data, system calibration scheme and control scheme are proposed, as well,
the serial-parallel computation strategy is designed based on CUDA. Finally, the effective-
ness and time efficiency of the system are evaluated through anthropometric experiments.
Experiment results show that the comprehensive error of scanning precision is less than
2 mm, and the complete scanning period is about 60 s.
Although the system proposed in this paper can quickly realize automatic scanning
of the human body, the occlusion of limbs and the blind area of measuring field of view
leads to holes in the point cloud, which needs to be filled manually by software at present.
Machines 2021,9, 299 12 of 13
Future work should be dedicated to combine deep learning technology to realize automatic
hole filling of scanned point cloud.
Author Contributions:
Conceptualization, X.Y. and X.C.; methodology, X.Y. and J.X.; software,
X.Y. and J.L.; data curation, X.Y., J.X., J.L. and X.C.; writing—original draft preparation, X.Y.;
writing—review
and editing, X.C. All authors have read and agreed to the published version of
the manuscript.
Funding:
This research was funded by Science and Technology Commission of Shanghai Municipality
(21511104202), the National Natural Science Foundation of China (52175478), and Shanghai Industrial
Coordination Leading Group Office project (2021-cyxt1-kj12).
Institutional Review Board Statement:
Ethical review and approval were waived for this study,
due to REASON (This work mainly aims at the application of clothing customization, the image
acquisition and 3D reconstruction are carried out on the premise of people’s normal dress, which
involves no biological experiments, and the whole process has no contact with people, and the system
components used have no radiation or other invisible damage).
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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