Download - 結合即時控制系統架構之 3D 介面 VR 系統分析
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
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System Architecture
Modeling VisualizationNumerical method
for computing data
Server Client
ModelingNumerical
MethodEfficiency Analysis
Character Data NetworkLOD
VisualizatiLOD
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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
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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
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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
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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