A QoS-Aware 3D Point Cloud Streamingfrom Real Space for Interaction in Metaverse
Hiroki Ishimaru1 , Yugo Nakamura2 , Manato Fujimoto3,4 , Hirohiko Suwa1,4 , Keiichi Yasumoto1,4
1

Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan
E-mail: {ishimaru.hiroki.ie3, h-suwa, yasumoto}@is.naist.jp
2
Kyushu University, Fukuoka, Fukuoka 819-0395, Japan
E-mail: y-nakamura@ait.kyushu-u.ac.jp
3
Osaka Metropolitan University, Osaka, Osaka 558-8585, Japan
E-mail: manato@omu.ac.jp
4
RIKEN, Chuo-ku Tokyo 103-0027, Japan

Abstract—In this paper, we present a point cloud streaming
method of real-space objects such as humans and animals forreal-time 3D reconstruction in VR space. The system uses a
depth camera to scan a human or animal, divides the point
cloud into parts of the body, and then controls the quality of thepoint cloud (i.e., resolution and frame rate) for each part in realtime according to the object’s motion and context. This enables
point cloud streaming with limited resources (computationaland network resources) and maximizes the user’s quality ofexperience (QoE). We have implemented and evaluated a seriesof systems incorporating the proposed method to enhance theuser experience in realistic environments and scenarios while
maintaining interactivity in VR-based online communication.
The results show that the proposed system is feasible underresource-constrained environments without significantly affecting
the user’s QoE.
Index Terms—Real-time communication, 3D point cloud, QoE,
Streaming, VR

I. I NTRODUCTION
In recent years, attention to the metaverse and digital twin
has increased, and how to incorporate real space situations into
virtual space has been actively discussed. However, because
real space is vast and complex, projecting it into the virtualworld is a difficult task. Especially the tourism field is oneof the most challenging use cases to virtualize because of theimportance of experiences and sensations in real space. In thevirtualization of tourist attractions, interactivity is essential torecreate the interaction with local people and animals, so it isideal for sharing space in real-time between virtual and real
space. To achieve real-time spatial sharing, real space must
be quickly scanned and projected in 3D into virtual space.
3D Point cloud data streaming is one promising approach toachieving this goal.
However, real-time streaming of 3D point cloud data has
resource limitations. The size of point cloud data is large,
and it is nearly impossible to stream the raw data on existing
mobile network infrastructure. For example, the point clouddata generated by the Azure Kinect depth camera is approximately 430 MB/s (720P/15FPS), which requires bandwidth at
the Gbps level. Furthermore, there are all kinds of locations
and environments in real space, and not all of them necessarily

have high-quality infrastructure. In particular, in the field of
tourism, which is our premise, the use of cellular networks
and mobile devices which have limited resources is essential.
Therefore, there is a need for an efficient point cloud streaming
method with low bandwidth consumption while maintaining
the quality of the user experience [1].
There are many studies and approaches to point cloudstreaming, including point cloud data encoding/decoding,tiling, and transmission optimization using view angle prediction. For example, V-PCC which standardizes point cloudcompression like a video compression standard [2], a prototype
for real-time point cloud capture and streaming on mobile
devices [3], and a client-driven dynamic point cloud streaming
system based on DASH (Dynamic Adaptive Streaming overHTTP) [4] have been proposed. However, most of these studies
focus on point cloud compression and preventing unnecessary
data transmission on a client basis. A few studies evaluate theimpact of these controls on user QoE [5]–[7].
In this paper, we propose an approach to extend point
cloud quality control to context-adaptive driving of objects
and to realize point cloud streaming on limited resources
while maintaining high QoE. In this paper, we describe theimplementation and evaluation results of the proposed systemand confirm that the system is capable of streaming point
clouds under cellular networks. The impact of the system’squality control on the user’s QoE is also investigated andevaluated.
This paper introduces a system that enables remote users tomove freely within a space and interact with local people andanimals without needing fixed infrastructure, despite remoteparticipation, in situations such as tourism where experienceand sensation in real space are important. The system is built
around the use of mobile devices so that it can be used at
any tourist site or location and enables users to easily use andparticipate in the system.
The main contributions of this paper are as follows:• Dynamic control of point cloud quality based on objects
in real space is realized.
• A prototype system with real-time quality control underresource limitation is implemented.

•

The impact of point cloud quality control on users’ QoE
is evaluated.
II. R ELATED WORKThis section describes existing research on XR collaborationand point cloud streaming as related studies.
A. XR Collaboration
Lee et al. proposed an approach that divides the real space
into OOI (Object Of Interest) and AO (Ambient Object) andcombines pre-reconstruction of OOI and real-time reconstruction of AO [8]. They proposed an approach that combines
OOI pre-reconstruction and AO real-time reconstruction. For
OOI, the posture (position and direction) information in spacedetected from the feature information of the object is sharedwith the remote side to reconstruct the polygon model in realtime. For AO, a point cloud masking the areas other than
OOI is generated and sent to the remote side, which is used
as a background during remote collaboration. WebSocket and
WebRTC were used to transmit the attitude information for
OOI and the point cloud information for AO, respectively.
Each of the information transfer methods is selected according
to the server load and data size. In addition, they show an
example of using a ball valve as an OOI, which is useful forcases such as obtaining operating instructions from a remoteexpert. They also propose a system that enables collaboration
between real physical space and virtual space with highreproducibility, but the use cases are limited due to the process
of OOI recognition and pre-modeling.
B. Point cloud Streaming
High decoding complexity exists in point cloud compression, and Li et al. proposed an approach to solve this complexity by transmitting uncompressed tiles of different quality
levels in addition to compressed tiles [6]. The system selects
an appropriate quality level for each tile that evenly divides
the point cloud and dynamically controls the trade-off between computational and communication resources. Although
uncompressed tiles consume more bandwidth, they do not
require decoding, thus reducing the amount of computation.
The authors also claim that the QoE can be optimized byapplying an approach that selects only tiles that exist within theuser’s FoV (Field of View) [9] , thereby reducing unnecessary
data transmissions, as well as by selecting the quality of thetiles. They measured the QoE of this system and found thatit achieved a higher QoE value than the conventional method.
They also propose a method for allocating communication andcomputational resources for point cloud video streaming andshow that it has superior performance compared to existing
methods. However, this method does not consider the quality
control of each detail of the target object according to the
movement and context of the object. Our system extends thatapproach to dynamically control point cloud quality (that is,
resolution and frame rate) according to the object’s motion
and context.

III. S YSTEM R EQUIREMENTS
We propose a system that enables remote and local users toshare space by scanning and transferring objects in real spacein real-time and reconstructing them in virtual space. In thissection, we describes the system’s prerequisites and associatedproblems and also introduce our envisioned use cases.
A. Prerequisites
We assume that the system will be used in the field of
tourism. This is because tourism is one of the most difficult use
cases to virtualize because of the importance of experiences
and sensations in real space. We believe that remotely interacting with people and animals in real space can be applied
to any situation. It is challenging since there are not manystudies targeting remote interactions with people and animals.
Several conditions are necessary to achieve the above goal.
There are two major problems: a high-speed network may not
be available, and using a fixed system is inappropriate.
First, let us discuss the problem related to high speed
network availability. Tourist destinations are widely scatteredfrom rural to urban areas and can be outdoors or indoors.
Therefore, we believe the proposed system must be widely
available in many locations. Hence, we assume the use ofcellular networks. According to data from NTT docomo, thelargest Japanese telecommunications carrier, effective cellular communication speeds in Japan from January to March2022 are 137Mbps to 273Mbps for download and 17Mbps
to 39Mbps for upload, which are not as fast as fiber-optic
lines [10]. Therefore, we considered systems with these transmission speeds in mind.
Next, we explain the problem that a fixed system is not
appropriate. We considered that a fixed installation of sensors
and processing equipment at the site would not be appropriate
from the standpoint of versatility. The reason for this is that
we assume that humans and animals are the target objects
when scanning objects in real space, but it is unlikely thatthey will remain in the same place for a long period of time.
To extend the range of using the system, we concluded thatthe system should be composed of mobile devices in the field.
From this condition, we consider that volunteers to handle
mobile devices are needed at the site. We assume that there
are volunteers at each tourist spot and that the system will be
realized with their cooperation.
B. Assumed Use Cases
We aim to make traveling and tourism more accessible byreproducing the local tourist experience in a virtual space andbuilding a system from which travelers and locals can benefit.
The system is expected to be used as follows:
1) Volunteers with mobile terminals equipped with LiDAR
are at the target tourist sites.
2) The remote user moves freely within the virtual space(provided in advance) corresponding to the sightseeing
spot and requests the projection of dynamic objects(people, animals, etc.) in the real space included in
his/her field of view onto the virtual space.

3) Upon receiving a request, the volunteer scans the object
with the mobile device’s LiDAR and sends it to thesystem.
4) The object is projected into the virtual space.
By placing a volunteer who scans objects between the
remote user and the local object, interaction with the local area
through the volunteer becomes possible. This enables users tohave an experience similar to that in real space when using
facilities or purchasing products. We believe these are potential
use cases in tourism and can also be applied to education,
training, and inspection tours.
C. Technical challenge
When the system is used in a mobile environment, various
problems arise. For example, network bandwidth and computing resources may be limited. To maximize QoE with limited
resources, factors that affect quality must be adjusted.
In this study, a depth camera is used to scan the target
object, but transmitting all the resulting point clouds in realtime would exceed the available bandwidth. For example, theAzure Kinect color point cloud stream uses approximately 430
MB/s bandwidth at 720P/15FPS, which cannot be transmitted
in real-time over cellular communication as described in Sect.
III-A. Compression of point cloud data is unacceptable ina mobile environment, as it can cause FPS degradation anddelays, and non-delivery of other data. In addition, the larger
the number of points in the point cloud data, the more it willaffect the processing time, which may also lead to a decrease inreal-time performance. Therefore, even when sending selected
point cloud data according to the OOI (Object Of Interest), it
is ideal to be able to reduce the data to the extent that it does
not significantly affect the user’s QoE.
IV. P ROPOSED S YSTEM
In this section, we describe the design of the system thatsatisfies the requirements described in section III-A and its
components, such as hardware and functions.
A. Approach
To realize online tourism, it is necessary to convert real
space with various tourist objects into real-time data andreconstruct it in virtual space. Still, it is difficult to acquire anduse all objects in real space in real-time due to computationaland network resources in mobile environments. Lee et al.
proposed an approach in which a portion of the real spaceis cut out, and only the necessary objects are reflected inreal-time, while static data is used for the other objects [8].
We adopted this approach in our system, dividing the real
space into the objects that the remote participants wanted
to project and the surrounding objects that would serve as
their backgrounds. Fig. 1 shows this approach. The necessary
objects and the surrounding objects are called OOI (Object Of
Interest) and AO (Ambient Object), respectively.
In addition, we considered that using cameras and communication infrastructure pre-installed at the site would beinappropriate in terms of ubiquity and versatility. Since there

Preloaded AO
Remote User

3D Reconstruction

Virtual World◀RemoteUser➁OOI Point cloud Stream➀Projection Request

Scanning

Real World

VolunteerFig. 1. Our approach in streaming real space objects to virtual space

are countless tourist spots, the system must ultimately be
flexible enough to accommodate them. Therefore, we assumed
that volunteers would be present in the vicinity of the target
real space. That OOI point cloud scanning and transmission
would be conducted with their cooperation, using their mobile
terminals, such as smartphones and tablets. Under these conditions, even when sending only OOI point clouds, there canbe delays that degrade the QoE in terms of communication
bandwidth and computational resources. Hence, we propose amethod to dynamically control the quality of the OOI point
cloud to keep the resources used under the limit. Unlike theprevious tiling approaches [6] divide the point cloud object
evenly, the proposed method recognizes the object’s attributeand divides the point cloud at the body part scale to perform
finer-grained quality control of the object to maximize the user
experience while reducing bandwidth consumption.
B. System Design
Based on the proposed approach, targeting tourism application, we propose a system that enables remote and local
users to share space by scanning and transferring objects
in real space and reconstructing them in virtual space inreal-time, without requiring fixed infrastructure. As shown in
Fig. 1, a volunteer at a sightseeing spot performs scanning anddata transmission of local objects (OOI) in the real space inreal-time at the request of a remote user. The real space isreconstructed in the remote user’s virtual space by using thetransmitted point cloud data of the local objects (OOI) together
with the background data (AO) prepared in advance, thereby
sharing the space of the tourist site is realized.
To reduce bandwidth consumption of the point cloud data
(OOI) handled by the system, we propose a dynamic quality
control based on body part movements and incorporate it in thesystem. Based on the techniques used in video compression
standards such as H.265 for 2D video and tiling approaches
for 3D point clouds, we reduce the total amount of point clouddata by dynamically varying the resolution and frame rate ofeach body part according to context: high frame rate/low resolution when the object part is moving significantly, and low
frame rate/high resolution when the part is nearly stationary.
Although the absolute quality of the point cloud data (OOI)
is lower than the raw data due to these processes, bandwidthconsumption is optimized by controlling the quality so as not
to affect the user’s QoE as much as possible.

C. Implementation

Masking

Color Texture

Scanning

3D Reconstruction

Depth Map

Pose Detection

Consider Quality

SenderQuality
ControlSenderQuality
Control

OOI Reconstruction

Divide into
body parts

Fig. 2. System Data Flow

The following is a description of the prototype system wedeveloped to evaluate the feasibility of the proposed system.
Fig. 2 shows a data flow of the system. The system is built
on ROS and uses Unity for 3D reconstruction.
First, we explain how to obtain the AO and OOI to reconstruct the real space in a virtual environment. First, we obtain
the AO by using tools such as a 3D scanning application thatutilizes LiDAR installed in the iPhone, or a camera that cancapture 360-degree images. In recent years, 3D scanning ofvarious locations has been progressing, so scanning of AO
objects may not be necessary in the future.
Next, we describe the actual method of acquiring OOI
point clouds for transmission and real-time reconstruction. As
mentioned above, we assume that mobile devices will be used
to acquire the OOI point clouds. The depth and texture of
the OOI are continuously acquired using a depth camera andtreated as a color point cloud. Our system uses Azure Kinect
as well as iPhone for evaluation.
After scanning an OOI object, quality control is dynamically
performed according to its context. First, as shown in Fig. 2,
pose estimation is performed on the 2D color video input fromthe camera to segment body parts and track their movement.
For the pose estimation, a trt-pose estimation model is used
for human pose estimation [11], [12], and a model trained by
DeepLabCut is used for animals (deer) pose estimation [13],[14], as shown in Fig. 3.

judges that the body part is moving and immediately reduces
the resolution to give priority to the frame rate. Conversely,
when the amount of movement of a body part remains below
the threshold value for several frames, the system determinesthat the object is near stationary and gradually increases theresolution (frame rate decreases).
The resolution of the point cloud is adjusted by resizing theoriginal input (color image, depth map: 720P) using nearest
neighbor completion (three steps: 25%, 50%, and 75%), andthe frame rate is switched by adjusting the transmission timing.
This process is performed for each body part. Table I shows
the bandwidth consumption of a sample human video sequencewhen our system controls the quality. In this sample, thebandwidth consumption (i.e., the maximum value) is 20.2
Mbps when all body parts are set to 75% quality, which
is within the possible transmission range with the existing
infrastructure.
The resulting point cloud data for each body part is thensent to Unity as a ROS topic and reconstructed in 3D together
with the AO prepared in advance. To ensure water tightness
in the representation of the point cloud, the size of the points
is dynamically changed according to the quality of the point
cloud. The reconstructed tourist attraction space is presented
to the user through a VR-HMD.
V. E XPERIMENTThis section describes experiments conducted to evaluate
the impact of quality control on user QoE.
A. Outline of Experiment
A 10%

OOI

B 25%

C 50%

720P

Source3D Sequence(Recorded: 30s)

D 75%

E 100%
F QoS

Randomize

Evaluation

Fig. 4. Quality Evaluation ExperimentFig. 3. In case of animals: masks are generated from animal pose estimation
results to get a point cloudContext-aware point cloud quality determination mechanism: The quality of the point cloud is determined by thevalue of the displacement of the corresponding body part. The
system constantly monitors the difference between the positionof the body part in the current frame and the previous frame.
When this difference exceeds a threshold value, the system

A: 0.1

B: 0.25

C: 0.5

D: 0.75

E: 1.0Fig. 5. Change in OOI due to Difference in Compression RatioTABLE I
S PECIFICATIONS OF POINT CLOUD QUALITY OUTPUT BY PROPOSED SYSTEM

25%
50%
75%

Head
0.28Mbps (13FPS)
0.9Mbps (12FPS)
1.8Mbps (9FPS)

Body
2Mbps (13FPS)
5.5Mbps (9FPS)
6.5Mbps (5FPS)

VR HMDArms
2.3Mbps (14FPS)
6.1Mbps (9FPS)
6.4Mbps (5FPS)

Legs
1.5Mbps (14FPS)
4.8Mbps (12FPS)
5.6Mbps (6.5FPS)TABLE IIS IGNIFICANT DIFFERENCES BY EACH POINT CLOUD SEQUENCE

Smoothness

Processed Sequence

Resolution

Fig. 6. Subjects wear VR HMD and view point clouds at 6DoF
Overall Quality

Fig. 4 shows an overview of the QoE evaluation experiment.
In this experiment, we asked users to view several different
quality levels of output from a point cloud sequence that we
had prepared in advance for about 30 seconds and to rate thequality of the output. The point cloud sequence consists ofthree states: human standing (stationary), waving, and stepping. Each output stream is based on the input source (720P)
with compression ratios of rate = 0.1, 0.25, 0.5, 0.75, 1,
respectively (Condition A-E). The compression ratio here is
the ratio of the resize to the video input to the system.
Nearest neighbor interpolation was used to resize both colorand depth video (For rate=0.1, the quality is 10% of 720P, i.e.,
128x72).Fig. 5 is an example of point cloud display for eachcompression ratio.
In addition to the above point cloud images, point cloud
images in which the quality was dynamically switched according to the context of the object were also subject toevaluation, resulting in a total of six patterns of point cloud
image evaluation (Condition F: Proposed system).
Fig. 6 shows a scene from the experiment. In this experiment, a VR headset (HTC VIVE) was used to view contents
in a 3D space.
B. Evaluation Methodology
The evaluation method was based on the ACR (Absolute
Category Rating) method [15], which is one of the representative video quality evaluation methods. After viewing a 30second evaluation video, the experimental participants rated
the quality of the video within the following 10 seconds on a
5-point quality scale (5.very good, 4.good, 3.normal, 2.poor,
1.very poor).
Since there is an order effect in which the quality of theimages is affected by the quality of the previous images,
the order in which the evaluated images are presented isAll
6.1Mbps
17.3Mbps
20.2Mbps

Two pairs to compare

P-valueSignificanceCompression A,FCompression B,FCompression C,FCompression D,FCompression E,FCompression A,FCompression B,FCompression C,FCompression D,FCompression E,FCompression A,FCompression B,FCompression C,FCompression D,FCompression E,F

0.008
0.790
0.002
0.000
0.000
0.001
0.007
0.244
0.020
0.001
0.046
0.138
0.129
0.026
0.012

!
!
!
!
!
!
!
!
!
!
!

randomized and varied for each participant in the experiment.
The three evaluation contents are as follows: (i) The qualityof the smoothness of the images, and (ii) Quality for thedenseness of the point cloud. (ii) Quality in terms of point
cloud density, (iii) Overall image quality.
The participants in the experiment were 15 undergraduate
and graduate students in their 20s. Each participant watched
six evaluation videos at random and rated them on a 5-point
scale immediately after watching each video.
C. Result
The quality evaluation results for each evaluation criterion
are shown in Fig. 7, Fig. 8, and Fig. 9. MOS was calculated
and visualized based on the evaluation results obtained fromthe experiment participants. First, the Kruskal-Wallis test wasused for the results of each evaluation criterion. The test results
showed significant differences in smoothness, resolution, andoverall quality in all three experiments. (p-value: Smoothness:
8.94 × 10−12 , Resolution: 1.14 × 10−9 , Overall Quality:
1.45 × 10−2 ) Thereafter, the Mann-Whitney U-test is used
to compare the quality of each criterion result. Table II show
the test results. The results of the U-test on the smoothness
quality results showed that condition F (proposed method)
was not significantly different from condition B (compression
ratio of 0.25). This result shows that the proposed method
has the same level of smoothness as condition B. The resultsof the U-test for resolution quality showed that condition F(the proposed method) was not significantly different fromcondition C (compression ratio of 0.5). This indicates thatthe proposed method retains the same level of resolution as
condition C. The results of the U-test on the overall quality

Quality for Smoothness

5

2

4
Rating scales

3

3
2

A

B

C
D
E
Type of ExperimentF

Fig. 7. Quality for Smoothness

1Overall Quality

5

4
Rating scales

Rating scales

4

1

Quality for Resolution

5

3
2

A

B

C
D
E
Type of ExperimentFig. 8. Quality for Resolution

results showed that condition F (the proposed method) was
not significantly different from condition B (compression ratioof 0.25) and condition C (compression ratio of 0.5). This
indicates that the proposed method maintains the same levelof overall quality as conditions B and C.
Condition B and C, which had the same overall quality,
were discussed in detail. Condition B had the same level ofsmoothness, but the proposed method was evaluated higher
in terms of resolution. Condition C has the same level ofresolution, but the proposed method has a higher evaluationin terms of smoothness. From these results, we believe thatthe proposed method is better in the individual evaluations,although the overall evaluation was at the same level.
VI. C ONCLUSION
In this paper, we proposed a point cloud streaming method
for real-time 3D reconstruction of real-space objects suchas people and animals in virtual space in order to realize
the virtualization of tourism. We proposed a dynamic quality
control approach that adjusts the resolution and frame rateof the point cloud according to the object’s motion, andimplemented and evaluated the proposed method. The resultsof evaluation experiments confirmed that the proposed method
was superior in individual evaluations, although the overall
quality of the proposed method was equivalent to that ofconditions B and C that degrade FPS/resolution equally for all
body parts. Although these results suggest the practicality of a
new compression method for point cloud images, it is unlikelythat the system will be effective for all types of users, and amore rigorous QoE evaluation that takes into account users’
preferences must be conducted. In addition to the displayof point clouds, the extent to which various interactions are
possible must also be considered.
ACKNOWLEDGMENT
This work is supported in part by the Commissioned Research of National Institute of Information and Communications Technology (NICT) in Japan, Contract No. 222C03 and
JSPS KAKENHI JP21H03431.
R EFERENCES[1] Z. Liu, Q. Li, X. Chen, C. Wu, S. Ishihara, J. Li, and Y. Ji, “Point cloud
video streaming: Challenges and solutions,” IEEE Network, vol. 35,
no. 5, pp. 202–209, 2021.

F

1

A

B

C
D
E
Type of ExperimentF

Fig. 9. Overall Quality[2] S. Schwarz, M. Preda, V. Baroncini, M. Budagavi, P. Cesar, P. A. Chou,
R. A. Cohen, M. Krivokuća, S. Lasserre, Z. Li et al., “Emerging mpeg
standards for point cloud compression,” IEEE Journal on Emerging and
Selected Topics in Circuits and Systems, vol. 9, no. 1, pp. 133–148,
2018.
[3] J. Hu, A. Shaikh, A. Bahremand, and R. LiKamWa, “Characterizing
real-time dense point cloud capture and streaming on mobile devices,”in Proceedings of the 3rd ACM Workshop on Hot Topics in Video
Analytics and Intelligent Edges, ser. HotEdgeVideo ’21. New York,NY, USA: Association for Computing Machinery, 2021, p. 1–6.
[Online]. Available: https://doi.org/10.1145/3477083.3480155[4] M. Hosseini and C. Timmerer, “Dynamic adaptive point cloud streaming,” in Proceedings of the 23rd Packet Video Workshop, 2018, pp. 25–
30.
[5] L. Wang, C. Li, W. Dai, S. Li, J. Zou, and H. Xiong, “Qoe-driven
adaptive streaming for point clouds,” IEEE Transactions on Multimedia,
2022.
[6] J. Li, C. Zhang, Z. Liu, W. Sun, and Q. Li, “Joint communicationand computational resource allocation for qoe-driven point cloud video
streaming,” in ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020, pp. 1–6.
[7] J. van der Hooft, M. T. Vega, C. Timmerer, A. C. Begen, F. De Turck,and R. Schatz, “Objective and subjective qoe evaluation for adaptive
point cloud streaming,” in 2020 twelfth international conference on
quality of multimedia experience (QoMEX). IEEE, 2020, pp. 1–6.
[8] Y. Lee, B. Yoo, and S.-H. Lee, “Sharing ambient objects using realtime point cloud streaming in web-based xr remote collaboration,” inThe 26th International Conference on 3D Web Technology, 2021, pp.
1–9.
[9] Z. Liu, S. Ishihara, Y. Cui, Y. Ji, and Y. Tanaka, “Jet: Joint
source and channel coding for error resilient virtual reality video
wireless transmission,” Signal Processing, vol. 147, pp. 154–162, 2018.
[Online]. Available: https://www.sciencedirect.com/science/article/pii/
S0165168418300185[10] “Effective speed measurement results.” (in Japanese) Accessed: 202212-13. [Online]. Available: https://www.docomo.ne.jp/area/effective
speed/[11] A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy,
M. W. Mathis, and M. Bethge, “Deeplabcut: markerless pose
estimation of user-defined body parts with deep learning,” Nature
Neuroscience, 2018. [Online]. Available: https://www.nature.com/
articles/s41593-018-0209-y[12] T. Nath*, A. Mathis*, A. C. Chen, A. Patel, M. Bethge, and M. W.
Mathis, “Using deeplabcut for 3d markerless pose estimation acrossspecies and behaviors,” Nature Protocols, 2019. [Online]. Available:
https://doi.org/10.1038/s41596-019-0176-0[13] B. Xiao, H. Wu, and Y. Wei, “Simple baselines for human pose
estimation and tracking,” in Proceedings of the European conferenceon computer vision (ECCV), 2018, pp. 466–481.
[14] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2dpose estimation using part affinity fields,” in Proceedings of the IEEE
conference on computer vision and pattern recognition, 2017, pp. 7291–
7299.
[15] P. ITU-T RECOMMENDATION, “Subjective video quality assessment
methods for multimedia applications,” 1999.