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

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Conference presentation of a paper: Andrey Smorkalov, Mikhail Fominykh, and Mikhail Morozov: "Stream Processors Texture Generation Model for 3D Virtual Worlds: Learning Tools in vAcademia," in 9th International Symposium on Multimedia (ISM), Anaheim, CA, USA, December 9–11, 2013, IEEE.

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

  1. 1. Stream Processors Texture Generation Model for 3D Virtual Worlds Learning Tools in vAcademia Andrey Smorkalov and Mikhail Morozov Volga State University of Technology, Russia Mikhail Fominykh Norwegian University of Science and Technology, Norway 9th International Symposium on Multimedia (ISM) December 9–11, 2013 Anaheim, CA, USA 1 VSUT
  2. 2. Outline o o o o o o o 2 Motivation and Challenges Related Work Texture Generation Model Original Methods Performance Evaluation User Evaluation Conclusions VSUT
  3. 3. 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 3 VSUT
  4. 4. Related work: Large Amount of Graphics in 3D VWs 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 4 VSUT
  5. 5. Web conferencing? 5 VSUT
  6. 6. 6 VSUT
  7. 7. 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. 7 VSUT
  8. 8. Interactive virtual whiteboard (VWB) of vAcademia 8 VSUT
  9. 9. 9 VSUT
  10. 10. Accessing tools 10 VSUT
  11. 11. 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 11 VSUT
  12. 12. Texture Generation Model: Mathematical Model (formalization) 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 12 VSUT
  13. 13. 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> 13 VSUT
  14. 14. 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 14 VSUT
  15. 15. Original methods for processing large amounts of graphics in 3D VWs o Sharing Changing Blocks ‒ Sharing application window ‒ Sharing web-camera image – Sharing video – Sharing screen area o Sharing Attributed Vector Figures ‒ Drawing figures and typing text – Inserting text o Processing Static Images ‒ Slideshow ‒ Image insert ‒ Sticky notes 15 – Area print screen – Backchannel VSUT
  16. 16. Original methods for processing large amounts of graphics in 3D VWs o Sharing Changing Blocks ‒ Sharing application window ‒ Sharing web-camera image – Sharing video – Sharing screen area o Sharing Attributed Vector Figures ‒ Drawing figures and typing text – Inserting text o Processing Static Images ‒ Slideshow ‒ Image insert ‒ Sticky notes 16 – Area print screen – Backchannel VSUT
  17. 17. Sharing application window 17
  18. 18. Drawing figures and typing text 18
  19. 19. Sticky notes 19
  20. 20. 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. 20 VSUT
  21. 21. 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. 21 VSUT
  22. 22. 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 22 VSUT
  23. 23. Testing performance with 50 VWBs 23 VSUT
  24. 24. Performance degradation as a function of the number of VWBs Performance 100% 99% 98% 97% Average 96% Peaking 95% 94% 93% 92% 0 24 10 20 30 40 Number of whiteboards 50 VSUT
  25. 25. Performance degradation as a function of the number of actively used VWBs Performance 100% 95% 90% Average 85% Peaking 80% 75% 0 25 5 10 15 20 25 Number of actively used whiteboards VSUT
  26. 26. Performance degradation as a function of the number of simultaneous changes of images on VWBs Performance 100% 96% 92% Average 88% Peaking 84% 80% 1 26 2 3 4 5 Number of simultaneous changes of images VSUT
  27. 27. 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 27 VSUT
  28. 28. Implications 28 VSUT
  29. 29. User Evaluation Question It was clear what functions the VWB has and how to access them. It was comfortable "to look" at VWBs (to change the view angle). VWBs displayed the contents crisply and precisely enough to understand them. VWBs displayed the contents quickly enough, and delays did not influence the process. Increasing the # of VWBs in the virtual auditorium during the class did not lead to visible delays. VWB is a convenient (handy) enough tool for working on similar tasks. Working with vAcademia tools is more comfortable than with traditional tools, for similar tasks. It was clear how to work in vAcademia. 29 Str. agree Agree 16 7 15 8 14 9 14 8 13 10 13 8 15 8 19 4 N VSUT 2 D SD
  30. 30. 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 30 VSUT
  31. 31. 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 31 VSUT
  32. 32. 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 32 VSUT

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