2021

[1]Shenghui Cheng

[1]Research Center for Industries of the Future and School of Engineering,
Westlake university, Hangzhou, China

2]Department of Computer Science, Stony Brook University, Stony Brook, USA

3]Key Laboratory of Coastal of Environment and Resources of Zhejiang Province,
Westlake university, Hangzhou, China

# Deepsea: A Meta-ocean Prototype for Undersea Exploration

Jinyu Li<EMAIL_ADDRESS>Ping Hu<EMAIL_ADDRESS>Weicheng Cui
<EMAIL_ADDRESS>Tianyi Huang<EMAIL_ADDRESS><EMAIL_ADDRESS>*  [  [

###### Abstract

Metaverse has attracted great attention from industry and academia in recent
years. Metaverse for the ocean (Meta-ocean) is the implementation of the
Metaverse technologies in virtual emersion of the ocean which is beneficial
for people yearning for the ocean. It has demonstrated great potential for
tourism and education with its strong immersion and appealing interactive user
experience. However, quite limited endeavors have been spent on exploring the
full possibility of Meta-ocean, especially in modeling the movements of marine
creatures. In this paper, we first investigate the technology status of
Metaverse and virtual reality (VR) and develop a prototype that builds the
Meta-ocean in VR devices with strong immersive visual effects. Then, we
demonstrate a method to model the undersea scene and marine creatures and
propose an optimized path algorithm based on the Catmull-Rom spline to model
the movements of marine life. Finally, we conduct a user study to analyze our
Meta-ocean prototype. This user study illustrates that our new prototype can
give us strong immersion and an appealing interactive user experience.

###### keywords:

Metaverse, Meta-ocean, virtual reality, marine reconstruction

##  1 Introduction

Metaverse is gaining increasing attention from researchers, developers, and
people from various industry area. Combining the physical reality and digital
virtuality 18 ; Cheng2023 , metaverse brings infinite possibility and
extensibility to scientific research, entertainment, education, etc
encyclopedia2010031 . The concept of the Metaverse was first proposed in a
piece of scientific fiction named Snow Crash in 1992 1 . As a computer-
generated universe, metaverse has been defined through vastly diversified
concepts All , such as lifelogging 2 , collective space in virtuality 3 ,
embodied Internet 4 , and Omniverse 6 .

Metaverse is a synthesis of a variety of techniques, including augmented
reality (AR), mixed reality (MR), virtual reality (VR), high-speed networks,
and edge computing All , among which VR techology 1960  has become a trendy
technology in recent yearscheng2022roadmap ; huang2023roadmap . VR technology
that simulates a virtual environment immersing users to have the feeling of
”being there” Wohlgenannt2020VirtualR , and the feeling of immersion must be
supported by the proper software and hardware. With the advances in computer
hardware and computing capability, the devices involved in VR environments
have become versatile, ranging from single head-mounted displays to several
complementary devices such as the EyePhone (a head-mounted display), the
AudioSphere (a sound system), and the DataGlove and DataSuit (for measuring
movements) 799723 ; Raitt1991TheEL ; Wohlgenannt2020VirtualR .

The early VR technology was mainly used for military training, flight
simulation, medicine MUHANNA2015344 , and so on by professional departments
due to its high-cost hardware limitation. Thanks to hardware improvement,
commercial VR devices such as Oculus Rift and HTC VIVE, had triggered the
diffusion of VR technology into private households Wohlgenannt2020VirtualR .
The applications of VR technology have gradually expanded into various fields,
such as the retail industry, energy industry, education industry, and tourism
industry. For example, in the retail industry, the IKEA company111IKEA
company: https://virsabi.com/ikea-virtual-reality-for-onboarding-and-
training/. uses VR technology to build visual employees to provide services to
customers. In the energy industry, the E.ON company222E.ON company:
https://www.smart-energy.com/industry-sectors/business-finance-regulation
/e-on-trafocusedubstation-workers-with-virtual-reality/. instructs their
substation workers to use VR tools to avoid safety accidents. In the education
industry, Google Expeditions articleGoogle  are used to make immersive
education. For the tourism industry articletourism , VR technology can
facilitate immersive site exploration and interactive tours for remote
visitors.

The field of visualization has always played an important role in academic and
industrial communities unknown ; huang2023high ; cheng2018colormap . As one of
the fundamental science and business direction, marine and ocean industry can
greatly benefit from the application of VR technology. Existing visualization
applications for marine science are mainly aiming to conduct the scientific
research, most of which are focused on certain ocean attributes. There are
many representative visualization applications for ocean related exploration
such as i4ocean articlei4ocean ; lili , google earth yu2012google , World Wind
bell2007nasa  and so on. However, these applications lack comprehensive
investigation and development for effective and immersive undersea exploration
and user-environment interaction. Furthermore, an application that empowers
people to freely explore the undersea environment with immersive interaction
is an important and urgent demand for both ocean education and ocean research.

To bridge this gap, we develop a novel application, Deepsea , for immersive
roaming in Meta-ocean with VR devices. In this prototype, we design and model
two types of ocean seabed scenes, the Indonesia Ocean and the Pacific Ocean,
respectively. Our prototype features our lifelike ocean creature modeling and
animation pipeline, realistic ocean scene rendering, and flexible undersea
exploration interaction. The technical details of our prototype are structured
as follows: the prototype design and each module details are described in
Section 2; the visual results, functions in the prototype, and implementation
details are presented in Section 3; a user study that is designed and
conducted for the prototype functions evaluation is reported in Section 4.

##  2 Design and functions

This section is organized in the following manner: We first describe the
overview of the design and functions of our prototype. Then we focus on two
aspects that are critical for undersea roaming, the design of the deep sea
environment and the behaviors provided in undersea roaming.

###  2.1 Overview

The architecture of Deepsea is shown in Figure 1. The hardware includes the
personal computer(PC) and the HTC Vive Pro headset. The HTC Vive Pro headset
includes a head-mounted display (HMD), two handheld controllers, and two
lighthouses. The handheld controllers are used to interact with the graphical
user interface (UI) in Deepsea. The right handheld controller is responsible
for emitting light rays to click a virtual button in order to choose the
functions. The left handheld controller is in charge adjusting the floating
and diving motion of the user. We use the SteamVR librarymurray2020building
to connect the headset and the PC.

Figure 1: System architecture

In the Unreal Engine, there includes four sub-modules: VR rendering module,
interaction module, UI module and ocean module. The VR rendering module
displays the virtual contents to the users in the VR scene with the help of
Vive Input Utility (VIU). The UI module provides text and image instructions
to the users and allows them to interact with the UI elements in the VR scene
with the help of the handheld controllers. The interaction module provides
interesting knowledge in the VR roaming scene. The ocean module includes the
marine biological models which are modeled according to real marine creature.

Figure 2: Data streaming and control signal flow in Deepsea runtime process

The flowchart of this prototype for roaming in the undersea is shown in Figure
2. In the first step, this prototype initializes the VR environment. The
prototype detects the VR hardware environment and connects the VR equipment
including the VR helmet and two handheld controllers. After that, the users
can enter the VR room where the parameter setting of the camera and
controllers can be initialized in a virtual environment. In the second step,
the prototype for roaming in the undersea begins to run. The functions are
chosen by users, then the prototype loads the static ocean scene according to
the users’ choice. The ocean phenomena and creatures are rendered to establish
the immersive ocean scene.

###  2.2 Modeling

For the modeling process, we can divide it into three parts, model making,
binding, and importing, as shown in Figure 3. Our model making utilizes two
prototype. We first select the picture and set the size of the model. Then we
implement the UV splitting and export it in FBX format from Maya into
Substance painter. Secondly, the prototype we used is substance painter. We
paste the maps which are made in the last part onto the model and adjust the
details according to the reference picture.

Figure 3: Pipeline for ocean creature modeling process.

In model binding, we use Maya to bind models. We first create the model bones
and implement the skinned Mesh. Then we paint the model weights and make the
animation frame. After that, we export the bones in FBX format from Maya. In
model importing, we use the Unreal engine to process the last part. First of
all, we import the models and bones built in the above parts into Unreal
Enginekaris2013real . After that, we create the material nodes and set the
number and position for the models. Next, we control the animation of models
and set the key points to format the motion path of the models. In the last,
we add the detectable collision bodies and interaction control for the models.

The models used in the Deepsea can be divided into two types. The first is the
ocean scene models which can be seen in Figure 4. Figures 4[a-c] are the real
ocean scene while Figures 4[d-f] are models used in Deepsea according to the
Figures 4[a-c]. From them, we can see that the Deepsea has restored the real
ocean scene as much as possible. The second is the marine creature as shown in
Figures 5. Figures 5[a-c] are the real marine creatures, which are used as
objects of reference in the process of modeling. Figures 5[d-f] show the bones
of marine creature models. Figures 5[g-i] are the final models used in the
Deepsea. From Figure 4 and Figure 5, we can find that there also exists a high
similarity between the real creature and the visual models, which can reflect
the effectiveness of our model processing.

Figure 4:  The comparison of the real ocean scene and the models. Figures(a-c)
are the real scenes in the seabed while the figures (f-i) are the modeling
scenes in the Deepsea. Figures(d-f) are model process with bones. Figure 5:
The comparison of the real marine creatures and the models

###  2.3 Adding behaviors

For marine creatures, their behaviors are mostly along the key points. We
first enter a path containing multiple keys and set their longitude, latitude,
height, and speed. Then we determine the starting point, the ending point, and
the waypoints according to the order of this path. In the process of path
travel, the perspective of the camera is always changed because it will turn
to the next node when a node is reached. As Figure 6 shows, A is the starting
point and B is the ending point. P1 and P2 are the waypoints.

Figure 6: Path optimization principle

In this figure, the purple arrow indicates the tangent of the point, and the
yellow box marked with view indicates the direction of the camera. The black
line represents the traditional polyline path while the blue curve represents
the roaming path. From Figure 6 we can see that the optimization path will
bring a better roaming effect for both marine creatures and users. In this
section, we propose a path optimization algorithm based on the Catmull-Rom
spline as shown in Figure 7.

Figure 7: Catmull-Rom spline algorithm diagram

As shown in Figure 7, the Catmull-Rom spline needs at least 4 points to
control. $P_{-1},P_{2}$ are the starting point and ending point while
$P_{0},P_{1}$ are the control points. The t is a parameter ranging from 0 to 1
to control the parallelism between the curve and the control points, which
will determine the fitting shape of this curve. The Catmull-Rom spline can be
defined by Equation 1 to Equation 3.

$P(u)=au^{3}+bu^{2}+cu+d$ (1)

$P(0)=P_{0},P(1)=P_{1}$ (2)

$P^{\prime}(0)=t(P_{1}-P_{-1}),P^{\prime}(1)=t(P_{2}-P_{0})$ (3)

To compare the path optimization effect of different curves, we set the six
key points to determine different paths according to different curve
algorithms. The key points can be seen in Table 3. We use the traditional
polyline path, the bezier curve path, and the catmull-rom curve path to
compare the effect of path optimization. As shown in Figure 8[a], we can see
that the traditional polyline path has the obvious corner in the connection of
key points, which will lead to stiff steering and uneven view change. Compared
with that, the paths in Figure 8[b] and Figure 8[c] both have better
smoothness and continuity. It is worth noting that the path generated using
Bezier curve did not coincide with key points in Figure 8[b]. This is
determined by the characteristics of the Bezier curve. The Bezier curve could
only be approximated infinitely but could be not pass through key points.
Compared with that, the path based on Catmull-Rom could not only be a smooth
Path but also pass through all key points as shown in Figure 8[c].

Table 1: 3D coordinate values of key points Key points | Longitude | Latitude | Height
---|---|---|---
0 | 121.47 | 31.23 | 10000
1 | 123.00 | 20.00 | 50000
2 | 135.00 | 10.00 | 50000
3 | 170.00 | 13.00 | 50000
4 | 180.00 | -5.00 | 50000
5 | 200.00 | -13.50 | 50000
|  |  |
Figure 8: Comparison diagram of different paths. a is the original polyline
path, b is the Bezier path, and c is the Catmull-Rom path

##  3 Results

The Deepsea is developed using the C++ programming language on Unreal
Enginekaris2013real  and run on a 3.00 GHz Intel Core i5-12490F processor,
16.0 GB RAM, and 12.0 GB dedicated GPU memory.

The flowchart of roaming in the Deepsea can be seen in Figure 9. We enter the
VR room which is supported by steam and begin to the undersea roaming with the
Deepsea. First of all, we can find the home page as shown in Figure 10. On
this page, we can choose a kind of seabed from the ocean of Indonesia and the
Pacific. The interaction way of this page need to use the control handles. The
Deepsea shapes the control handles into jellyfish, and users can emit rays to
the menu which can choose the functions.

Figure 9: Flowchart of roaming in the undersea with Deepsea for users Figure
10: Home page of Deepsea

The second step is roaming the undersea. The Deepsea provides two sea scenes
which are the Indonesia ocean and the Pacific ocean as shown in Figure 11 and
Figure 12. Users can use the left handle to control the direction of roaming
and use the right handle to emit ray determining functions. We set different
models according to the real sea area and strive to provide a kind of
immersive and real roaming experience to users.

Figure 11: The Indonesia ocean in Deepsea Figure 12: The Pacific ocean in
Deepsea

In the process of roaming, we provide some interactions to users as shown in
Figure 13. There are some interesting questions about the current sea area.
Users can choose answers by emitting rays using the right control handle as
shown in Figure 13[a-b]. Besides, users can select marine creatures by
emitting rays while they are roaming in undersea. If the ray exactly selects
the target creature, there are text boxes occuring and showing the detailed
knowledge about that creature for users as shown in Figure 13[c]. In order to
add the immersion, we also add the auto-voice system for this function.
Finally, there also have exit options. For example, if the roaming time of
users is more than five minutes, a prompt box will occur which will remind
users that the energy is exhausted and they need to exit as shown in Figure
13[d].

Figure 13: The interaction in Deepsea

##  4 User Study

This section is organized as follows: First, we design a few roaming tasks and
two questionnaires to examine the users’ enjoyment and engagement. Second, we
report the time of completing roaming tasks and the total scores of each
questionnaire. Finally, we examine the relationships between the time to
complete the roaming task and the performance of users based on the above-
mentioned evaluation.

###  4.1 Participants

We invite 50 healthy participants to take part in our user study. The
participants involve 22 males and 28 females, whose mean age is 25.2 years
old. Besides, we also set strict healthy exclusion criteria in order to avoid
any discomfort in using the VR device, i.e., we exclude people with
significant cognitive dysfunction, previous neurological illness psychiatric,
heart condition, other serious medical conditions, significant mobility
restrictions, seizures, or with interfering medical devices. We also exclude
pregnant woman and elderly individuals.

###  4.2 Tasks and questionnaires

We set roaming tasks in the seabed scene for participants in order to test the
simplicity and effectiveness of Deepsea during the roaming. We set a few key
points in the scene as a roaming path and record the time used in roaming, the
total number of collisions, and the accuracy with the UI rays. For accuracy,
we record the total number and the effective number of emitting rays to UI,
then we can calculate the accuracy using the above numbers. The results can be
seen in Table 2. We make statistics of results and calculate the mean value
and the standard deviation(SD). From Table 2 we can find that the time used in
roaming and the number of collisions are lower than the numerical value that
we expected. Besides, the lower SD also reflects that there exists strong
stability in the experience of roaming with Deepsea. Besides, the third
variable, accuracy with the UI rays, also shows the effectiveness of UI
module.

Table 2: Results of roaming tasks Variable | Mean
---|---
Time used in roaming (s) | 213.4
Number of collisions | 2.3
Accuracy with the UI rays | 0.75
|
Table 3: Statements used to quantify users’ enjoyment Variable | Statement to be graded | Mark | Mean
---|---|---|---
| I was interested in tasks | 1-5 | 4.6
| I found it impressive | 1-5 | 4.3
Enjoyment | I felt skillful | 1-5 | 3.2
| I felt completely absorbed | 1-5 | 4.5
| I found it easy to use | 1-5 | 4.1
Sum |  | 5-25 | 20.7
|  |  |

There are two questionnaires designed to evaluate the enjoyment and engagement
of participants. The first questionnaire consists of nine statements, which
mainly focus on the enjoyment of participants. The participant could choose a
level of their agreement with using a five-point scale (not at all, slightly,
moderately, fairly, and extremely). Responses of participants were associated
with a score ranging from 1 to 5. The second questionnaire is made up of six
questions that mainly focus on the engagement of participants. The
participants were asked to use an ”X” to fill in the appropriate box of a
five-point scale according to their experience. For all questionnaires, the
participants were also prompted to consider the entire scale to answer the
questions independently, and not skip questions or return to a previous
question to change their answers. The results of the questionnaires can be
seen in Table 3 and Table 4.

According to the feedback from the participants, most people positively think
they experience a strong sense of immersion. Besides, participants agree that
they can focus on the tasks given the interaction functions. On the other
hand, participants hold various opinions regarding how natural the interaction
in this prototype is. The results of the questionnaires can be seen in Table 3
and Table 4. Overall, we can find that the Deepsea can bring enjoyment and
engagement for participants

Table 4: Questions used to quantify users’ engagement Variable | Question to be graded | Mark | Mean
---|---|---|---
| How involved were you in the virtual environment experience? | 1-5 | 3.8
| Please rate your sense of being in the Deepsea. | 1-5 | 4.1
Engagement | How natural did your interactions with the Deepsea seem? | 1-5 | 3.6
|  | How much did you feel the handheld controllers were your own
---
hands?
1-5 | 3.7
|  | How well could you concentrate on the assigned tasks rather
---
than on the mechanisms used to perform those tasks?
1-5 | 3.7
Sum |  | 5-25 | 18.9
|  |  |

###  4.3 Hypotheses and statistical analysis

In this section, we have conducted an analysis to explore the connection
between the enjoyment and engagement scores of users with using a
questionnaire-based evaluation. Subsequently, we investigated whether the
engagement score exhibited any correlation with two specific task-related
variables: the time taken to complete the task, the total number of collisions
and accuracy with the UI rays.

####  4.3.1 Hypotheses

We set the following hypotheses to analysis the correlation of the Deepsea and
the tasks.

H1: Participants’ enjoyment and engagement scores are positively related.

H2: Participants’ performance and their engagement score are related.

Hypotheses H1 and H2 are investigated through the completion of tasks. Based
on previous studies on serious games, we expect to find a correlation between
enjoyment and engagement. Thus, we set a positive hypothesis like H1. If such
a correlation exists, it will provide valuable insights for future design
interventions in the VR platform, aiming to enhance enjoyment and subsequently
improve engagement. In H2, our hypothesis posit that there will be an
association between participants’ performance measures, such as the total time
taken to complete the experiment, the total number of collisions, and accuracy
with the UI rays, and their engagement score. We anticipat that if such an
association is observed, it will demonstrate the potential of using VR for
operations management training.

####  4.3.2 Analysis of engagement, enjoyment and performance

We first employ the Kolmogorov–Smirnov (K-S) test to confirm whether the
variable data is a normal distribution. The results are as follows: The
engagement scores (D=0.107, p=0.2), enjoyment scores (D=0.124, p=0.054), the
time taken to complete the task(D=.056 ,p=0.2 ) and the accuracy with the UI
rays (D=0.106, p=0.2) are all normally distributed. And the total number of
collisions (D=0.287, p=0) is not following a normal distribution.

Then, for each Hypotheses we will choose the appropriate algorithms to explore
the correlation between variables. To test H1, we employ the Pearson
correlation coefficient to determine the correlation between enjoyment scores
and engagement scores (r = 0.732, p = 0.0). The scatter plot in Figure 14
provides visual evidence of this correlation. The statistically significant
correlation coefficient confirms the existence of a strong association between
enjoyment scores and engagement scores, thereby validating hypothesis H1.

To test H2, we employ the Pearson correlation coefficient to determine the
correlation between engagement scores and the total number of collisions (r =
-0.7, p = 0), as well as the correlation between engagement scores and
accuracy with the UI rays (r = 0.737, p = 0). These correlations are based on
the assumption that the data followed a normal distribution. The statistically
significant correlation coefficients confirm a relatively strong association
between engagement scores and both the total number of collisions and accuracy
with the UI rays. The scatter plots in Figure 15 and Figure 17 provide visual
representations of these correlations. Since the total number of collisions
did not follow a normal distribution, we use the Spearman correlation
coefficient to determine the correlation between collisions and engagement
scores (r = -0.795, p = 0, Figure 16). This analysis has confirmed the
existence of a correlation between collisions and engagement scores. Overall,
our findings align with our expectations and support hypothesis H2.

Figure 14: Scatter plot of enjoyment and engagement scores. Points correspond
to individual realizations and area between two gray lines represents the 95
confidence interval of the linear regression. Figure 15: Scatter plot of time
and engagement scores. Points correspond to individual realizations and area
between two gray lines represents the 95 confidence interval of the linear
regression.  Figure 16: Scatter plot of collision and engagement scores.
Points correspond to individual realizations and area between two gray lines
represents the 95 confidence interval of the linear regression.  Figure 17:
Scatter plot of accrancy and engagement scores. Points correspond to
individual realizations and area between two gray lines represents the 95
confidence interval of the linear regression.

In this section, we have conducted a user study to gather feedback and
insights from users regarding their experience with Deepsea. Our primary focus
is to examine the relationship between enjoyment and engagement scores, which
serve as indicators of user evaluation and interaction within the Deepsea
platform. Figure 14 visually demonstrates that higher levels of proficiency in
Deepsea operations are associated with increased enjoyment. This finding
underscores the importance of improving user proficiency in operations to
enhance their overall perception of Deepsea.

We first record various aspects of users’ operations in Deepsea, including the
total time taken to complete the experiment, the total number of collisions,
and accuracy with the UI rays. Then we proceed to analyze the correlation
between these variables and the users’ engagement scores using either Pearson
or Spearman correlation analyses. Notably, Figure 15 to Figure 17 visually
illustrate the significant relationships observed between engagement scores
and these operational variables.

Based on these findings, future efforts will focus on providing prototype pre-
training for users and improving the overall user experience. This will
include the creation of pre-training tutorials, user-friendly prototype
enhancements, optimization of collision detection using artificial
intelligence algorithms, and offering hardware improvement suggestions to
hardware providers. These initiatives aim to further enhance user proficiency
and interaction smoothness within Deepsea.

##  5 Summary and conclusion

In this paper, we review the relevant technological development of the Meta-
ocean and realize the importance of building the visual environment using VR
technology. Then we design and build our own visual environment, Deepsea,
which can provide an immersive experience for users when they explore the
Meta-ocean. We model the ocean scenes and use the Catmull-Rom spine to
optimize the roaming path in order to increase immersion. Finally, we
conducted a user study to investigate users’ experiences with Deepsea and
analyze potential future directions.

Metaverse has already become a hot trend in various fields while ocean
exploration has also attracted attention from academics and governments. The
Meta-ocean, as an extension of the above topics, will certainly evolve into a
key issue and change the life of humans. Using VR technology, we propose the
Deepsea to make a tentative exploration of the Meta-ocean. This prototype has
given an immersive and delightful experience for users. In future work, we
will continue to optimize the interaction module and model more ocean scenes
to build more immersive Meta-ocean applications. The Deepsea will be a useful
tool for exploring the Meta-ocean.

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