2005/06/22 NCSLAB 1

結合即時控制系統架構之3D介面 VR 系統分析

電機系控制組(D92921003)黃雋博(R93921067)彭詩淵

2005/06/22 NCSLAB 2

Outline

Motivation System Architecture Technique A case study Conclusion

2005/06/22 NCSLAB 3

Motivation

Interactive game environment real time system which

can interact with other people

Keeping the system maintain the display rate with 25 frame/s.

Client-1Client-2

Server

Screen in Client-2

Screen in Client-1

2005/06/22 NCSLAB 4

Motivation

In the on-line interactive VR system, the delay will be occurred in many situations. Therefore, the timing analysis is very important for this project. The delay events that we predict are as bellow:

Delay in server: Calculation time (modeling, numerical)Separate the whole environment for individual client

Delay in network time:Conjunction delayCompensation

Delay in client:Rendering (According to hardware of the system)Request for the server

2005/06/22 NCSLAB 5

System Architecture

Modeling VisualizationNumerical method

for computing data

Server Client

ModelingNumerical

MethodEfficiency Analysis

Character Data NetworkLOD

VisualizatiLOD

2005/06/22 NCSLAB 6

Technique

Control over Quality, Bandwidth, Buffer Condition, CPU’s Utilization, Scheduling Techniques for real time-systems

(Ex: Rate Monotonic (RM) Algorithm, Earliest Deadline First (EDF) Algorithm)……

2005/06/22 NCSLAB 7

Technique

Video Encoder

Encoder Buffer

Bandwidth Analysis

Rate Control

Server

Physics ModelNumerical

Method

Environment Generator

Video Decoder

Decoder Buffer

Environment Visualisation

System Efficiency

Client

2005/06/22 NCSLAB 8

Technique

Teb: encoder buffering delay Tdb: decoder buffering delay Te: encoding delay Td: decoding delay Tc: networking delay Tnm: numerical method computing delay Tpm: physics model computing delay Tv: visualization delay Teb+Tdb+Te+Td+Tc+Tnm+Tpm+Tv 40ms≦

2005/06/22 NCSLAB 9

Technique

Level of Details (LOD) technique

LOD rendering techniques reduce the geometric complexity of 3D models, sacrificing visual rendering quality in order to increase frame rendering rates.

Physically-based: based on physical lows or mathematical functions --- complex but real

Non physically-based game : do not use physical laws--- simple but unreal Try the best to reduce

the high computational costs and maintain the reality

2005/06/22 NCSLAB 10

A case study

Non-physical based method Grassland by [Neyret 1998]

translates texture to show the animated grassland

[ Neyret 1998 ]

LOD example---Grassland [Perbet and Cani 2001]

highest LOD model

middle LOD model

lowest LOD model

2005/06/22 NCSLAB 11

Physical based models

yy

x

P

Cantilever beam model [Anjyo et al. 1992]

Mass spring damped model [Miller 1988] Mass-spring model[Provot 1995]

3 21( ) ( )

6 2

P PLy x x x

EI

( ) i ii i i

i

l dlF k L l D

dtl

A case study

1

1

1

i i

i i

i i

m mm mi

m m

X XF k L X X

X X

2005/06/22 NCSLAB 12

A case study

Set spring between the masses Assume the position of i th

particle is

the position of i+1 th particle is

the internal force between the i th spring is

Each particle has gravity, wind resistance

and buoyancy

( , , )i i i im m m mX x y z

+1 +1 +1 +1( , , )i i i i

m m m mX x y z

+1

+1

+1

[ ] i i

i i

i i

m mm mi

m m

X XF k L X X

X X

Use Euler’s method

( )F

a t tm

( ) ( ) * ( )v t t v t t a t t

( ) ( ) * ( )p t t p t t v t t

2005/06/22 NCSLAB 13

A case studyOriginal grass

particlesReduce

computational costVisualization using

spline model Different types of grass Number of

masses of each grass

Computational timefor one frame

Line visualization 20 69812.5 ms

Line visualization 4 17.2 ms

Spline visualization 4 81.3 ms

1000-Grass ENVIRONMENT

Bird viewLOW LOD

HIGH LOD

2005/06/22 NCSLAB 14

For(i=0; i<GrassNum ; i++) { IF ( (ViewerPOS.Z- grass[i].Z)<HighLODRange) ) grass[i]HighLODVisualization Else grass[i]LowLODVisualization }

X

Y

Z

2005/06/22 NCSLAB 15

A Case Study

Different Types of grasslands

Simulation time for one frame

Line visualization (Low LOD)

15 ms

Spline visualization (High LOD)

80 ms

combine Line and Spline visualization

50.0 ms

For Client / Sever

1000-Grass ENVIRONMENT

733MHZ Pentium III CPU

Different Types of grasslands

Simulation time for one frame

Line visualization (Low LOD)

17.2 ms

Spline visualization (High LOD)

81.3 ms

combine Line and Spline visualization

34.4 ms

3GHZ Pentium IV CPU

2005/06/22 NCSLAB 16

Scheduling

Task TS TN TC

Pk PS PN PC

ek eS(q(k)) eN(q(k)) eC(q(k))dk dS(q(k)) dN(q(k)) dC(q(k))

dS dN dC

PeS eN eC

dS dN dC

PeS eN eC

dS dN dC

PeS eN eC

ModelingNumerical Method

CompressionBuffering TimeTransmission

Visualization

TS TN TC

Server Network Client

2005/06/22 NCSLAB 17

Scheduling

EDF

dS(1) dN(1)dC(1)

P(2)

dS(2) dN(2)dC(2)

P(3)

eN(3)

dS(3) dN(3)

P(4)

dC(3)dS(4)

eS(1)

40ms

eS(3) eS(4)eC(2) eC(3)eC(1)

eS(2)eN(2)eN(1) eN(4)

dN(4)

dS(1) dN(1)dC(1)

P(2)

dS(2) dN(2)dC(2)

P(3)

dS(3) dN(3)

P(4)

dC(3)dS(4)

eS(1)

40ms

eS(3) eS(4)eC(3)eC(1)

eS(2)eN(2)eN(1) eN(4)

dN(4)

P(1)

eC(2)eN(3)

P(1)

2005/06/22 NCSLAB 18

Experiment

Particle: 6240 vs. 14040 Mean 6240 14040

Computing Networking Drawing Computing Networking DrawingLow Quality 9.6304 12.4823 5.0051 49.8278 17.419 6.2772

Optimal 9.6911 12.3418 14.3076 49.7051 17.2848 15.0646High Quality 9.7443 27.3797 26.2772 48.9734 20.1722 28.7709

Std 6240 14040Computing Networking Drawing Computing Networking Drawing

Low Quality 7.6106 6.7135 7.3016 6.3438 6.141 7.6786Optimal 7.5973 6.6538 4.3826 6.112 6.0646 3.2316

High Quality 7.5978 13.6864 7.3217 5.3516 47.8047 48.8173

2005/06/22 NCSLAB 19

Experiment - Low Quality

0 50 100 1500

50

100

150

200computing 6240

0 50 100 1500

50

100

150

200Networking 6240

0 50 100 1500

50

100

150

200Drawing 6240

0 50 100 1500

50

100

150

200computing 14040

0 50 100 1500

50

100

150

200Networking 14040

0 50 100 1500

50

100

150

200Drawing 14040

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Experiment - High Quality

0 50 100 1500

50

100

150

200computing 6240

0 50 100 1500

50

100

150

200Networking 6240

0 50 100 1500

50

100

150

200Drawing 6240

0 50 100 1500

50

100

150

200computing 14040

0 50 100 1500

50

100

150

200Networking 14040

0 50 100 1500

50

100

150

200Drawing 14040

2005/06/22 NCSLAB 21

Experiment - Optimal

0 50 100 1500

50

100

150

200computing 6240

0 50 100 1500

50

100

150

200Networking 6240

0 50 100 1500

50

100

150

200Drawing 6240

0 50 100 1500

50

100

150

200computing 14040

0 50 100 1500

50

100

150

200Networking 14040

0 50 100 1500

50

100

150

200Drawing 14040

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Conclusion

The concept of LOD is used to maintain the human impression under real-time requirement.

The timing analysis for real-time has been established.

the rate control for large number of the VR scenario have been discussed.

2005/06/22 NCSLAB 23

References

[Neyret 1998] F. Neyret, “Modeling, animating, and rendering complex scenes using volumetric textures,” IEEE Transactions on Visualization and Computer Graphics, Vol. 4, No. 1, pp. 55–70, January 1998.

[Perbet and Cani 2001] Frank Perbet and Marie-Paule Cani ,“Animating praires in real-time ,” Proceedings of the 2001 symposium on Interactive 3D graphics, March ,2001

[Anjyo et al. 1992] K. Anjyo, Y. Usami, and T. Kurihara, “A Simple Method for Extracting the Natural Beauty of Hair,” Proceedings of the 19th annual conference on Computer graphics and interactive techniques, Vol. 26, No. 2, pp. 111-120, Chicago, IL, USA, July 1992.

[Miller 1988] G. S. P. Miller, “The motion dynamics of snakes and worms,” Proceedings of the 15th annual conference on Computer graphics and interactive techniques, Vol. 22, No.4, pp. 169-178, Atlanta, GA, USA, August 1988

[Provot 1995] X. Provot, “Deformation constraints in a mass-spring model to describe rigid cloth behavior,” Proceedings of the Graphics Interface, pp. 147-154, Québec, QC, USA, May 1995.

[J. Vieron and C. Guillemot 2004] J. Vieron and C. Guillemot, “Real-time constrained TCP-compatible rate control for video over the Internet,” IEEE Transactions on Multimedia, Vol. 6, Issue 4, pp. 634-646, 2004.

[J. Bai et al. 2002] J. Bai, Q. Liao, X. Lin, and X. Zhuang, “Rate-distortion model based rate control for real-time VBR video coding and low-delay communications,” Signal Processing: Image Communication. Vol. 17, No. 2, pp. 187-199, 2002.

[D. Wu et al. 2000] D. Wu, Y. T. Hou, and Y. Q. Zhang, “Transporting Real-Time Video over the Internet: Challenges and Approaches,” Proceedings of the IEEE, Vol. 88,  Issue 12,  pp. 1855-1877, 2000.

[B. Li and K. Nahrstedt 1999] B. Li and K. Nahrstedt, “A control-based middleware framework for quality-of-service adaptations,” IEEE Journal on Selected Areas in Communications, Vol. 17,  Issue 9, pp. 1632-1650,1999.

[C. Lu et al. 2002] C. Lu, J. A. Stankovic, G. Tao, and S. H. Son, “Feedback Control Real-Time Scheduling: Framework, Modeling, and Algorithms,” Real-Time Systems Journal, Special Issue on Control-theoretical Approaches to Real-Time Computing, 23(1/2): 85-126, July/September 2002.

[Baldi and Ofek 2000] M. Baldi and Y. Ofek, “End-to-end delay analysis of videoconferencing over packet-switched networks,” IEEE/ACM Transactions on Networking, Vol. 8, Issue 4, pp. 479-492, Aug. 2000