a motion-aware approach to continuous retrieval of 3d objects (icde 2008) mohammed eunus ali rui...
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A Motion-Aware Approach to Continuous Retrieval of 3D Objects
(ICDE 2008)Mohammed Eunus Ali
Rui Zhang
Egemen Tanin
Lars Kulik
Department of Computer Science and Software EngineeringUniversity of Melbourne, Australia
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Outline
Applications and problem
Our Motion-Aware approach
Data representation and retrieval
Buffer
Index
Experiments
Conclusion
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A smart phone to see the interior of restaurants
Applications
Emerging more complex applications, e.g., tours using augmented reality
A rescue officer can see the structure of a building even if the building is on fire and filled with smoke
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Problem
Continuous retrieval of 3D objects in a window
Model: client-server
Bottle neck: bandwidth, especially when the view is moving fast
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Observation
QtQt+1
Qt+2
Qt+3
Qt+4
Qt+5
Qt+6
Speed
Speed
Speed
QtQt+1
Qt+2
Qt+3
Qt+4
Qt+5
Qt+6
A continuous query from a mobile client
The details can be determined using the client’s motion
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Motion-Aware Approach
Motion-aware data retrieval (overall)
representing 3D objects in multiple resolutions (wavelets)
only retrieving necessary resolution (speed)
incremental retrieval (windows, resolutions)
Motion-aware buffer management (client)
prefetching
caching
Index for 3D objects in wavelets (server)
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Base meshBase meshBase meshBase mesh
Progressively including detailsProgressively including detailsProgressively including detailsProgressively including details
Multi-resolution Representations
Figure: http://research.microsoft.com/~hoppe/
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Example Wavelet Decomposition
Base Mesh (M0) Mesh (M1)M0 M1
v1v2
v3
v1v2
v3
v1v2
v3
v4
v5v6
v4
v5v6
Wavelet coefficient, d4 = v4 – (v1+v2)/2 = v4 – v′4
v′4
v′5v′6
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Example Wavelet Decomposition
Mesh (M1)
v1v2
v3
v4
v5v6
Mesh (M2)
v1v2
v3
v4
v5v6
v’12
v’14
v’15
v’10
v’13
v’11
v’8
v’7
v’9
v1v2
v3
v4
v5v6
v12
v14
v15
v10
v13
v11
v8
v7
v9
M1 M2
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1 2
3
4
56
Incremental Retrieval (window)
Q t-1
Q t
A B
CD
A’ B’
C’D’
E
F
G
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Data Retrieval in Multiple Resolutions
Ot Qt Qt-1
Nt Qt - Qt-1
rt MapSpeedToResolution (st )
If ( Ot ) then
If (rt > rt-1) thenR Retrieve( {(Ot, rt-1, rt ), (Nt , 0, rt )} )
R Retrieve( {(Nt, 0, rt )} )
R Retrieve( {(Qt , 0, rt )} )
else
else
Algorithm: ContinuousRetrieval
QtQtQt-1Qt-1
Qt-1
Qt-1
Qt-1
Qt
Qt
Qt
Qt
Qt
Qt-1
rt
rt-1
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Motion-Aware Buffer Management
• We have a high-latency environment with decent computational capacity
• Cache and pre-fetch objects that are very likely to be retrieved along the path of a client
• Kalman-Filter is used in target tracking
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Prediction
Qt
Qt+10.5
0.2
0.3
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Buffer: Given probabilities to move in one dimension to two directions
Find: nopt
Will maximize the Average Residency Time!
Buffer Assignment in One Dimension
nopt
1 a-1
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p2
p3
p4
p1
pl = p1 + p2 pr = p3 + p4
a-1
nl nr
n1 n2
pl = p1 pr = p2
Buffer Assignment: Generalized
n3 n4
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Indexing 3D objects in Wavelets
A Naïve method: using a 4D R-treePosition of the wavelet coefficient and Magnitude of the coefficientEach vertex needs a number of neighbor vertices tooRetrieval is a two step process: Retrieve those coefficients that fall inside query window Extend the query window to retrieve the neighbors
Our method: indexing the support region
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Support Regions of Wavelets
v1v2
v3
v4
v5v6
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An Efficient Access Method
w
x
y
Query : ( R, 1.0, 0.7 )
Query : ( R, 0.7, 0.0 )
w = 1.0
w = 0
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Experimental Setup
A city is augmented by complex 3D objects such as spheres, pyramids.
Three-dimensional objects are decomposed using wavelet-based techniques and stored in a server.
Clients make a tour in the city from a randomly selected source towards a destination: Using a Tram or on Foot
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Experiment Parameters
Data size: 20MB, 40MB, 60MB, 80MB
Query Frame: 5%, 15%, 15%, 20% in height and width
Wireless bandwidth 256Kbps and latency 200ms
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Continuous Retrieval
Effect of speed on data retrieval
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Index
Effect of speed
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Buffer Management
(a) Cache hit rate (b) Data utilization
Effect of buffer size
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Overall System Performance
(a) Tram (b) Walk
Query response time (Uniform)
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Overall System Performance
(a) Tram (b) Walk
Query response time (Zipf)
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Conclusions & Future Work
We proposed a motion-aware approach to continuous retrieval of 3D objects. Experiments shows that the motion aware techniques outperforms traditional ones and overall we achieve high improvement, especially when the view is moving fast.
Future work:Server-side buffer managementReflecting pathways in indices