Stream processors texture generation model for 3d virtual worlds learning tools in vacademia

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Stream Processors Texture Generation Model for 3D Virtual Worlds

Learning Tools in vAcademia

9th International Symposium on Multimedia (ISM) December 9–11, 2013

Anaheim, CA, USA

Andrey Smorkalov and Mikhail Morozov

Volga State University of Technology, Russia

Mikhail Fominykh

Norwegian University of Science and Technology, Norway

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Outline

o Motivation and Challenges o Related Work o Texture Generation Model o Original Methods o Performance Evaluation o User Evaluation o Conclusions

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Motivation and challenges: Applying 3D VWs for learning

o 3D Virtual Worlds (VWs) – Have great features… … but not widely used

o Challenges – Steep learning curve – Demand for computational and network resources – lack of features that educators use in everyday teaching

o Solution Proposal – Enabling learning scenarios which require large amounts

of 2D graphical content displayed VSUT

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Related work: Large Amount of Graphics in 3D VWs

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o Multiple workspaces or virtual screens … but their performance is limited o Small number of active screens (Second

Life has a limit of five) o Static images (Sametime 3D has a sticky

notes tool, but notes are static, placed on slots, constant size, and no other tools on the same screen

o Individual use of screens

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Web conferencing?

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Related work: Current technological limitations

Usually, an image is calculated on a CPU on client side (e.g., in Second Life™ and Blue Mars™) or server side (e.g., in Open Wonderland™) and then loaded into the stream-processor memory as a texture. Therefore, the use of dynamic 2D images in existing 3D VWs is very limited.

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Interactive virtual whiteboard (VWB) of vAcademia

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Accessing tools

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Texture Generation Model: Motivation

o CPU ‒ CPU is loaded maintaining 3D environment ‒ source data for the synthesis of images and the data area for the

resultant images are in the local memory of other devices

o Stream processors ‒ 3D visualization is hardware-based and conducted on SPs ‒ SPs’ computing power usually exceeds the capabilities of CPUs

tenfold

o Challenge ‒ SPs have hardware limitations which do not allow to use them for

implementing most of the classical image processing algorithms

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Texture Generation Model: Mathematical Model (formalization)

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o Defining – Image, Transformation, Figure, Rasterization, Projected figure

o And configurable functionality o texture sampling, color mask, hardware cut of the rasterization

area, hardware-based blending of the source image and the rasterized image

o Calculating parts of image (even single pixels instead of the whole image)

o Comparing the efficiency of different approaches to any specific task

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Texture Generation Model: Programming Model

The programming model and architecture are based on four main objects o Texture – image stored in SP memory o Drawing Target defines resultant image o Filter – subroutine returns color in coords. o Filter Sequence – sequence of Filters and limiting condition <β>

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Texture Generation Model: Programming Model

o Modification of the DWT Algorithm for SPs ‒ Original modification of the Discrete Wavelet

Transformation (DWT) algorithm to run on SPs ‒ We applied the method of 2D DWT filter cascade

o Rasterising Attributed Vector Primitives on SPs ‒ SPs are able to deal only with vertexes and triangles ‒ We use a specific optimized method for triangulating

figures

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Original methods for processing large amounts of graphics in 3D VWs

o Sharing Changing Blocks ‒ Sharing application window – Sharing video ‒ Sharing web-camera image – Sharing screen area

o Sharing Attributed Vector Figures ‒ Drawing figures and typing text – Inserting text

o Processing Static Images ‒ Slideshow – Area print screen ‒ Image insert – Backchannel ‒ Sticky notes

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Original methods for processing large amounts of graphics in 3D VWs

o Sharing Changing Blocks ‒ Sharing application window – Sharing video ‒ Sharing web-camera image – Sharing screen area

o Sharing Attributed Vector Figures ‒ Drawing figures and typing text – Inserting text

o Processing Static Images ‒ Slideshow – Area print screen ‒ Image insert – Backchannel ‒ Sticky notes

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Sharing application window

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Drawing figures and typing text

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Sticky notes

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Performance Evaluation

I. Comparison of the algorithm performance on SPs and CPU

II. General efficiency of the system

We present average results acquired by running the system on ‒ 20 different hardware configurations with Intel CPU and

NVidia / ATI graphics adapters from the same price range ‒ On each hardware configuration 10 runs were conducted for

each image size.

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Performance Evaluation: I. Algorithms on SPs and CPU

The rationale behind using SPs (instead of CPU) for image processing in vAcademia is confirmed. The improvement differs from the ratio of the peaking performance of SPs to the peaking performance of CPU not more than twofold, which can be considered satisfactory.

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Performance Evaluation: II. General Efficiency of the System

Tested: performance degradation as a function of the number of: o VWBs (in one location) o actively used VWBs o simultaneous changes of images on

VWBs

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Testing performance with 50 VWBs

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Performance degradation as a function of the number of VWBs

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92%

93%

94%

95%

96%

97%

98%

99%

100%

0 10 20 30 40 50

Performance

Number of whiteboards

AveragePeaking

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Performance degradation as a function of the number of actively used VWBs

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75%

80%

85%

90%

95%

100%

0 5 10 15 20 25

Performance

Number of actively used whiteboards

AveragePeaking

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Performance degradation as a function of the number of simultaneous changes of images on VWBs

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80%

84%

88%

92%

96%

100%

1 2 3 4 5

Performance

Number of simultaneous changes of images

AveragePeaking

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User Evaluation

o Diagram designing task using provided templates

o 23 second-year CS students o No tutorials on vAcademia were

given o All participants had experience

playing 3D video games o Data: system logs, questionnaires,

and an interview VSUT

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Implications

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User Evaluation

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Question Str. agree Agree N D SD It was clear what functions the VWB has and how to access them.

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It was comfortable "to look" at VWBs (to change the view angle).

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VWBs displayed the contents crisply and precisely enough to understand them.

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VWBs displayed the contents quickly enough, and delays did not influence the process.

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Increasing the # of VWBs in the virtual auditorium during the class did not lead to visible delays.

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VWB is a convenient (handy) enough tool for working on similar tasks.

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Working with vAcademia tools is more comfortable than with traditional tools, for similar tasks.

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It was clear how to work in vAcademia. 19 4

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Conclusions

o Original method for collaborative work with large amount of graphical content in 3D virtual worlds

o Design & implementation in vAcademia o The algorithms we applied

– are superior to the commonly used ones

o The tools we designed – have stable work and – have educational value

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Future Work

o Designing scenarios for new learning activities possible using our method

o Conducting a full-scale user evaluation testing all designed tools

o Developing new tools based on our method

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Thank you!

Andrey Smorkalov smorkalovay@volgatech.net

Mikhail Fominykh mikhail.fominykh@ntnu.no

Mikhail Morozov morozovmn@volgatech.net

http://vacademia.com

http://www.facebook.com/vAcademia

@vacademia_info

http://slideshare.net/vacademia

http://slideshare.net/mfominykh

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