AN INTERACTIVE 3D VISUALIZATION MODEL BY LIVE

STREAMING FOR REMOTE SCIENTIFIC VISUALIZATION

Eriko Touma, Satomi Hara, Mari Kurumi, Yuri Shirakawa, Chisato Ishikawa, Masami Takata

Graduate School of Humanities and Sciences, Nara Women’s University, Nara-city, Japan

Takeshi Horinouchi, Kazuki Joe

Research Institute for Sustainable Humanosphere, Kyoto University, Uji-city, Japan

Graduate School of Humanities and Sciences, Nara Women’s University, Nara-city, Japan

Keywords:

3D Visualization, Live Streaming, GPGPU, Geophysical Fluids.

Abstract:

Recent improvement of high-end GPUs has made it possible to perform real-time 3D visualization such as

volume rendering and 3D contour plot for scientiﬁc data locally. A web browser based remote 3D visual-

ization by visualization servers is attractive, but data transfer overhead prevents from performing interactive

operations. We propose an interactive remote 3D visualization model by live streaming for geophysical ﬂuids

research. In this model, we use live streaming ﬂash media for the web browser based operations keeping mini-

mum quality of data analysis and minimum bit rate for live streaming of ﬂash media. Preliminary experiments

with a prototype system validate the effectiveness of our proposing model.

1 INTRODUCTION

Visualization is an effective and attractive tool for an-

alyzing numerical data of scientiﬁc phenomena. To

ﬁnd out a peculiar phenomenon from huge amount

of numerical data, visual understanding is sometimes

more efﬁcient than just numerical analysis by com-

puters. Thus, visualization techniques are to be in-

dispensable in the ﬁelds where huge numerical data

analysis is ineluctably faced. In recent years, even

researchers without deep knowledge of 3D visualiza-

tion theory can make use of 3D visualization such as

volume rendering and 3D contour by using 3D visu-

alization software. For the actual and effective use of

3D visualization, they must prepare a high-end com-

puter and install required software packages and/or

plug-ins of which number is sometimes considerably

large. These limitations prevent new users of scien-

tists from using 3D visualization for their research. If

3D visualization environments were easily available

for any researchers such as desktop-publishing tools,

the number of new users of 3D visualization would

increase drastically. Considering current client-server

environments or grid computing, it is natural to think

of 3D visualization servers: any remote users can use

3D visualization servers through a 3D visualization

web browser.

Web3D technologies, which have been paid atten-

tion to in recent years, are modeling based, i.e. poly-

gon based rendering. It is mostly used for product

advertisement and is not suitable for scientiﬁc visual-

ization, which is volume based, i.e. voxel based ren-

dering. Regardless to say, the volume based render-

ing is more computationally intensive than the mod-

eling based rendering. This is the reason of having the

3D visualization servers. Although the improvement

of enhanced low cost PC is remarkable, it is still far

from the performance of real time volume rendering,

for examples.

To get interactive 3D visualization of scientiﬁc

data, the response time of 3D visualization is essential

rather than the resultant quality. Just the 3D visual-

ization servers are inefﬁcient because of data transfer

overhead. In short, typical 3D visualization servers

are not suitable for real time 3D visualization in sci-

entiﬁc research. We are interested in an interactive

3D visualization environment via a web browser for

geophysical ﬂuid science in which huge data must be

treated.

In this paper, we propose an interactive 3D visual-

ization model by live streams for remote scientiﬁc vi-

sualization via a web browser. By using the proposed

model, users can apply volume rendering and 3D con-

tour to their target data located in a remote database

409

Touma E., Hara S., Kurumi M., Shirakawa Y., Ishikawa C., Takata M., Horinouchi T. and Joe K. (2008).

AN INTERACTIVE 3D VISUALIZATION MODEL BY LIVE STREAMING FOR REMOTE SCIENTIFIC VISUALIZATION.

In Proceedings of the Third International Conference on Computer Graphics Theory and Applications, pages 409-414

DOI: 10.5220/0001095404090414

 SciTePress

via the web browser. Visualized objects displayed

on the browser can be observed from any direction

with any scales interactively. GPGPU is adopted for

the calculations of real-time volume rendering and 3D

contour plot. The proposed model is implemented as

a 3D visualization function for Gfdnavi introduced in

section 2, which is a tool to produce web-based data

services for geophysical ﬂuid sciences. The 3D visu-

alization function is based on our 3D visualization im-

plementation model(E. Touma and Joe, 2007) which

enables huge data to be analyzed easily, quickly, and

efﬁciently.

The rest of the paper is organized as follows. In

section 2, we introduce Gfdnavi, a tool to construct a

data server for geophysical ﬂuid sciences. In section

3, we propose a web browser based 3D visualization

function for Gfdnavi. The model of interactive 3D

visualization via a web browser is the key concept in

our proposal to be described in section 4.

2 GFDNAVI

A group of geophysical ﬂuid scientists has re-

cently developed a software package named Gfd-

navi(T. Horinouchi and Shiotani, 2007)

. It is a tool

to produce web-based data services for geophysical

ﬂuid sciences. The package of Gfdnavi includes a

special-purpose web-server program, so one can start

up web-based data services just by installing it. It

also runs with commonly-used web servers such as

Apache. Therefore, it is suitable both to use within

local hosts and to operate servers to make data pub-

licly available. Also, it is suitable to share data among

a group of scientists. Gfdnavi creates a metadata

database of numerical data automatically by scanning

a tree of local directories under the top directly that a

Gfdnavi-server maintainer speciﬁes. It supports a few

ﬁle formats frequently used in geophysical ﬂuid sci-

ences. Gfdnavi enables its clients to search, analyze,

visualize, and download data with web browsers. It

is implemented by using the web development frame-

work Ruby on Rails. This framework naturally en-

forces the Model-View-Controller (MVC) architec-

ture to the products constructed with it, where the

model typically represents a relational database. The

metadata database of Gfdnavi archives the structure

of the directory tree. It also supports searches by text

and spatio-temporal parameters such as searches us-

ing global maps.

In Gfdnavi, metadata extraction from numerical

data is done through GPhys(T. Horinouchi and Take-

http://www.gfd-dennou.org/arch/davis/gfdnavi/

hiro, ), a class library in the Ruby language to handle

multi-dimensional data of geophysical ﬂuids. It ab-

stracts data formats based on a data model close to the

one used in NetCDF, thereby enabling uniﬁed access

to data in multiple formats such as GRIB and GrADS.

A Gfdnavi user ﬁrst selects numerical data either

from the directory tree or from the metadata search.

Then the data can be visualized. Currently, it sup-

ports visualization up to 2D. Gfdnavi does not require

any plug-ins, so a user can use it only by using a web

browser.

Currently, a Gfdnavi server provides data on local

disks. However, a development is underway to sup-

port search and use of data across multiple Gfdnavi

servers. It is envisioned to create an overlay network

of Gfdnavi to realize virtual uniﬁcation geophysical

ﬂuid data access across the network.

3 3D VISUALIZATION

FUNCTION FOR GFDNAVI

As described in the previous section, Gfnavi provides

just 2D visualization functions. Using 3D visualiza-

tion, it is possible for geophysical ﬂuid scientists to

understand a lot of information at a time intuitively.

For this purpose, we implement the 3D visualization

functions in Gfdnavi. In this section, we explain the

design concept of 3D visualization function for Gfd-

navi with some details of required elemental tech-

nologies.

For the appropriate implementation, we enumer-

ate our design concept below.

i) Efﬁcient analysis of huge data

ii) Simple operations without any manual

iii) OS-independent

iv) High performance visualization

v) Web browser based operations

vi) Interactive operability

We use our 3D visualization implementation

model (E. Touma and Joe, 2007) for the implemen-

tation of 3D visualization functions to satisfy i) and

ii). We do not explain the detail of the implementa-

tion model because of lack of space.

For the concept iv), we take the advantage of

GPGPU. It is natural that the surplus computing abil-

ity is used for a part of calculation which should be

performed by CPUs, if GPUs perform programmable

pipeline operations. Nowadays, GPGPU is rapidly

used for various visualization tasks. We also use

GPGPU for Gfdnavi 3D visualization functions: vol-

ume rendering and 3D contour plot.

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410

The 3D visualization function of Gfdnavi should

also provide a web browser based user-interface, and

it satisﬁes the concept v). The 3D visualization re-

sults are displayed by Flash animation to satisfy iii).

In our implementation of web browser based 3D vi-

sualization functions, target objects should be rotated,

zoomed and moved by simple mouse operations, re-

gardless to say, interactively. It satisﬁes the concept

vi).

4 REMOTE 3D VISUALIZATION

BY LIVE STREAMING

3D visualization was a computationally intensive task

sometimes requiring supercomputers. As the im-

provement of GPU technologies, now it comes to be

almost an interactive task for reasonably small local

data. During the design of 3D visualization func-

tion of Gfdnavi, we have faced a difﬁcult implementa-

tion problem. As explained in section 2, Gfdnavi is a

tool for client-server or grid computing environments.

Adding 3D visualization functions to Gfdnavi with

a web browser based method, we need to determine

who visualizes target data. In the case that a server

performs the computation, the server needs to transfer

a lot of resultant snap shot images to clients rapidly.

Obviously there is a problem of data transfer over-

head to perform the interactive remote 3D visualiza-

tion. On the other hand, when a client (a web brows-

ing PC) performs the computation, the client must be

equipped with a latest GPU as well as GPGPU ap-

plication environments. Furthermore, the client must

download the whole target data in advance of the

computation. Since the purpose of Gfdnavi is to pro-

vide geophysical ﬂuid scientists with various analy-

sis functions as well as data, the former approach of

server rendering is preferable.

4.1 3D Visualization Model for Remote

Data

In our method, visualization runs on a visualization

server, which means server side rendering. The vi-

sualization results are delivered to client users as live

streaming movie. In other words, client users can ob-

serve the visualization results as real time animation

via a web browser. When a point of display area in

the web browser is clicked or dragged by mouse, an

operation command is transmitted to the visualization

server. The visualization server reﬂects the transmit-

ted operation command so that it changes visualiza-

tion results: rotate, move and zoom, interactively. In

Browser

Server

for visualization

(2)

Data

3D visualization

software

(3)

(4)

Search 3D visualization

(6)

(1) (5)

(7)

(8)

(9)

(10)

Figure 1: The ﬂow of the interactive 3D visualization pro-

cess.

this way, interactive 3D visualization for remote data

by streaming distribution is obtained.

Figure 1 brieﬂy explains the ﬂow of the interactive

3D visualization process. A user accesses to a Gfd-

navi server via a web browser. The Gfdnavi server

contains data for geophysical ﬂuid research, which

can be browsed from the data search page shown as

(1) of ﬁgure 1. When the user chooses the data that

the user wants to analyze, an operation command is

send to the Gfdnavi server as shown in (2). In (3), the

Gfdnavi server sends the information about the data

that the user chose. The information is displayed in

(1). When the user executes 3D visualization, the user

goes to the page of a visualization server as shown

in (4) and (5). The information of data chosen and

the request of 3D visualization by the user are sent

to the visualization server at (6). The visualization

server asks the Gfdnavi server for the data and re-

ceives the data as shown in (7) and (8). After down-

loading the whole data, 3D visualization is applied

to the data according to the user’s requests. The vi-

sualization server takes snap shot images of the 3D

visualization results at a regular interval. An anima-

tion clip is generated from these snap shot images to

be distributed as a streaming Flash ﬁle at (9). In the

browser of (5), the user can observe the visualization

result by animation delivered from the visualization

server in live streaming. When the user clicks and

drags by mouse on the stream animation display area,

the information of the mouse position and operation

is sent to the visualization server as a command to re-

quest the corresponding visualization process such as

rotating, moving, and zooming. The resultant visual-

ization is delivered back to the client in live stream-

ing, namely interactively. In the same way, the user

can request a visualization method of 3D contour plot

or volume rendering as well as parameter setting in

real time. When the user wants to change data, the

user goes back to the data search page shown as (10).

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VISUALIZATION

411

4.2 Advantage of the Model

In our proposing model, geophysical ﬂuid scientists

can observe the results of 3D visualization via a web

browser without installing any visualization packages

and with the beneﬁt of high-end 3D visualization re-

sources. Live streaming by Flash animation is OS-

independent. Although the installation of a plug-

in is required for watching the Flash animation, the

Flash player is a popular plug-in and easy for users

to install. Actually, most web browser users have in-

stalled the Flash player plug-in because Flash media

is widely common. The beneﬁt of streaming media

is that clients need not download the movie data into

their hard disks. It saves client users from considering

about the storage capacity to watch as long movies as

they want.

In our model, the process of 3D visualization,

movie generation of visualization results, and live

streaming distribution is performed on a visualization

server. In this process, it is unavoidable that dropping

frames sometimes occur because of regular sampling

of snap shot images. During scientiﬁc data analysis

with 3D visualization, the essential requirement is to

guarantee that scientists surely recognize the display

changes caused by their operations. In our model, a

display of a 3D visualization result does not change

unless a new command is issued. Therefore, drop-

ping frames are not a critical problem for our model

unlike stream distribution of animation which always

changes their display.

4.3 Implementation

4.3.1 Interactive 3D Visualization for Remote

Data

The 3D visualization functions executed in visualiza-

tion servers include volume rendering and 3D con-

tour plot by GPGPU with Cg as a shader language.

The Cg language can be used from OpenGL APIs.

For volume rendering and 3D contour plot, we use

VTK, which supports OpenGL as low level APIs. We

develop the 3D visualization functions in Cg so that

they are callable from any VTK program

as well as

Gfdnavi. Taking the advantage of collaboration with

GPGPU, the 3D visualization functions of the visual-

ization server will achieve high performance visual-

ization.

We here explain the performance issue. Namely,

how fast GPGPU based 3D visualization is? As

We know VTK5.2 will ofﬁcially support Cg and GLSL,

but we don’t like to wait for the future release.

for volume rendering by GPGPU, it has been re-

ported that the frame rate of displaying 166MB vol-

ume data consisting of a (512,512,333) array on an

Nvidia Quadro FX 3400 GPU is 15.3fps(F. Schulze

and Hadwiger, 2007). In the case of 3D contour plot,

it has been reported that the frame rate of displaying

588MB volume data consisting of a (700,700,600) ar-

ray on an ATI Radeon X800 with 256 MB memory

is 28.7fps(B. von Rymon-Lipinski and Keeve, 2005).

Although the frame rates change by the complexity of

the volume data, considering the reported GPUs are

relatively old, the real-time 3D visualization (30fps)

is almost possible on the latest high-end GPUs as de-

scribed in the next subsection.

In our model, the visualization server receives op-

eration commands from client users, and the 3D vi-

sualization functions are invoked by the commands.

Receiving commands, if necessary, the visualization

server needs to get target data from a DB server (may

be equivalent to a Gfdnavi server). The data dealt with

geophysical ﬂuid research mainly consists of four di-

mension arrays, and is so large in size. Downloading

the whole data in advance of 3D visualization to the

visualization server, the waiting time may sometimes

irritate client users. To solve the problem, we use the

Gphys library of Gfdnavi, which clips the three di-

mension domain speciﬁed by users. Clipping just the

necessary data, the data size is reduced to shorten the

data transfer time.

In our implementation, we adopt a double trans-

fer scheme assuming that the network bandwidth be-

tween the visualization server and the Gfdnavi server

is enough high such as two nodes in a PC cluster with

a high speed interconnection network. According to

a data transfer request, when the target data is larger

than the pre-deﬁned size, Gphys is invoked to clip and

performs partial data transfer. At the same time, the

whole data is transferred to the visualization server

in background. This background data transfer is ex-

pected to complete until the client user makes another

clip request after several visualization operations. If

so, the new clip is obtained in local, i.e. from the

pre-loaded whole data in the visualization server. If

not so, the requested clip is transferred from the Gfd-

navi server again keeping the background whole data

transfer. In this way, we ensure the interactive opera-

tion for web based client users keeping from the huge

data transfer overhead.

4.3.2 Live Streaming

3D visualization results are taken as snap shot images

with 320× 240 at every

seconds. An mpeg ani-

mation is generated from those images at every sec-

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412

Server

for visualization

3D visualization

software

Client

Streaming server

FLV fileSWF file

Movie

Figure

Browser

Figure 2: The ﬂow of the live streaming distribution.

(1)

(2) (3)

Click the mouse

Figure 3: The experiment results.

ond using ImageMagick. Then, the mpeg data is con-

verted to FLV using FFmpeg. In order to distribute

the FLV data in a streaming form, we set up a Flash

streaming server with red5. A Flash SWF player is

created to run in a client web browser. ActionScript is

used to call and play the FLV data that is in the visual-

ization server. ActionScript also performsthe acquisi-

tion of mouse event information on the Flash player.

The other interface parts, buttons and sliders for pa-

rameter regulation, are implemented in JavaScript.

The ﬂow of the live streaming distribution is shown

in Fig. 2.

In our system, the recommendation bit rate for

watching the live streaming animation is 518K bps.

The recommended bit rate is calculated by BPP ×

f ps× width× height, where BPP is 0.225 that is the

right value for sports or music video. Note that the fa-

mous ”YouTube” adopts 500Kbps as the recommen-

dation bit rate, so our recommendation bit rate is not

a special (too expensive) case.

4.4 Experiments

To validate our model, we perform experiments using

a prototype system. First, we show the effectiveness

of interactive operation under some constraints. Then

we show the high performance visualization of real-

time volume rendering.

The prototype consists of two PCs connected

by typical LAN. The client is a standard desktop

PC while the server is a PC with Pentium D CPU

(2.66GHz X 2) and 512MB memory. The client PC

has a web browser with a Flash player where mouse

events are acquired to send commands to the server.

The server performs a simple volume rendering pro-

gram written with VTK. The volume data is a small

3D array of 144 by 73 by 30 (2.48MB). Receiving

a command from the client, the volume data is ro-

tated to generate ten snap shot images as a BMP for-

mat with the size of 320 by 240 (230KB per image).

Those BMP ﬁles are converted into an mpeg movie

by ImageMagick, and the mpeg movie are also con-

verted to an FLV movie (9KB for 10 frames assum-

ing 500Kbps) by FFMPEG. After the creation of the

FLV ﬁle, ten BMP ﬁles and one mpeg ﬁle are deleted.

Then, the server sends a message to notify the client

of the creation of the FLV ﬁle. When the client re-

ceives the message, the Flash player refreshes the dis-

play to load the new streaming FLV movie.

In the client, mouse events acquisition, com-

mand issuing and movie display are performed by

ActionScript while display refresh is performed by

JavaScript. The server and the client communicate

in socket connection to exchange messages. All the

tasks in the server are controlled by a Perl script.

Figure 3 shows the experiment results. A visual-

ization result is displayed as Flash video in (1). By

mouse dragging as shown in (2), a new visualization

result is displayed in (3). In this way, the interactive

3D visualization by live streaming is shown to be pos-

sible.

In this experiment, the response time from a

mouse click at the client to the re-display is 4.071

seconds. We explain each process time among the

response time. It takes 0.103 seconds from the mouse

click to the beginning of command issue to the server.

In the server, ten executions of volume rendering re-

quire 1.46 seconds. Ten image ﬁles creation takes

0.21 seconds. Generating an FLV ﬁle from ten BMP

ﬁles requires 1.133 seconds. The load time for the

new stream is 0.14 seconds. The rest of 1.025 sec-

onds is communication overhead.

The longest time consuming task is the volume

rendering part of 1.46 seconds. Note that this vol-

ume rendering is performed in a CPU (not using

GPGPU). To conﬁrm the real-time 3D visualization,

we perform the second experiment. Using GPGPU,

we apply volume rendering to a volume data set

with the size of 512 by 512 by 512 (134.7MB).

We obtain the performance of 19.78fps and 4.77fps

with Quadro FX4500 (512MB) and GeForce7300GS

(512MB) , respectively. Using the latest high-end

GPUs, we surely have faster 3D visualization than

30fps. Namely, we can expect ten times volume ren-

dering for (512,512,512) volume data requires less

than 0.3 seconds.

The second longest time consuming task is the

creation of ten image ﬁles (0.21s) and one FLV ﬁle

(1.133s). In this experiment, we use several existing

AN INTERACTIVE 3D VISUALIZATION MODEL BY LIVE STREAMING FOR REMOTE SCIENTIFIC

VISUALIZATION

413

①10 Frame

FLV creation

①10 Frame

Visualization

①10 Frame

Streaming

②10 Frame

Visualization

②10 Frame

FLV creation

②10 Frame

Streaming

③10 Frame

Visualization

③10 Frame

FLV creation

・・・

GPU

CPU 1

CPU 2

0.3 s. 0.3 s. 0.3 s.

Figure 4: The pipeline chart for visualization process, FLV

creation and live streaming.

applications for this task, so the data transfer is done

via ﬁles. Re-implementing this task within memory,

we estimate the whole process will be reduced to one

over ten (0.13s).

The rest of time consuming task is communication

overhead of 1.025 seconds. This delay can be surely

improved because the synchronization operations are

performed by a Perl script. Replacing this part with

MPI synchronization functions, this overhead will be

almost eliminated.

4.5 Discussions

The preliminary experiments indicate the following

analyses.

1) Using the latest GPUs, ten times volume render-

ing for a (512,512,512) volume data set is esti-

mated at 0.3 seconds.

2) Creation of ten frames FLV movie requires less

than 0.3 seconds.

3) Assuming 500Kbps bit rate, the communication

delay of ten frames FLV movie is also less than

0.3 seconds.

4) On the client web browsers, the detecting time for

mouse events is 0.1 seconds by ActionScript.

If we use a visualization server with dual core

CPUs as well as a high-end GPU, the delay between a

client web browser and the visualization server is 0.3

seconds as shown in ﬁgure 4. In the case of mouse

events, the delay is estimated at 0.4 seconds. Thus

we conclude that we can develop an interactive 3D

visualization system by live streaming for remote sci-

entiﬁc visualization within the delay of 0.4 seconds,

which is enough fast response time for geophysical

ﬂuid research.

5 CONCLUSIONS

In this paper, we proposed an interactive 3D visualiza-

tion model using live streaming so that the 3D visu-

alization is applied to scientiﬁc data located in other

computers connected by the Internet. The model is

to be implemented as 3D visualization functions for

Gfdnavi, which is a tool to produce web-based ser-

vices for geophysical ﬂuid sciences. For this purpose,

we described six design concepts for the implemen-

tation. Based on the design concepts, we explained

the implementation methods using GPGPU and live

streaming. To validate our implementation method,

we developed a prototype system to perform prelim-

inary experiments. The experimental results indicate

that our interactiveremote 3D visualization system by

live streaming would have the delay of 0.3 seconds,

provided with a high-end GPU and a dual core PC,

while only 500Kbps bit rate streaming is enough for

the remote 3D visualization. Our future work includes

the actual implementation of our proposed system.

ACKNOWLEDGEMENTS

This work was partly supported by the Ministry of Ed-

ucation, Culture, Sports, Science and Technology of

Japan, Grant-in-Aid for Scientiﬁc Research on Prior-

ity Areas, g i-explosionh(No 18049043).

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