deep learning for surface reconstruction · 2018. 3. 30. · the proposed deep learning som will be...
TRANSCRIPT
SHAFAATUNNUR HASAN
SITI MARIYAM SHAMSUDDIN
UTM BIG DATA CENTRE,
UNIVERSITI TEKNOLOGI MALAYSIA,
81310 SKUDAI JOHOR
DEEP LEARNING FOR SURFACE RECONSTRUCTION
1.Universiti Teknologi Malaysia is an innovation-driven entrepreneurial Research University and a leading research-intensive university in engineering, science and technology ranked in the top 100 world ranking in engineering and technology.
2.It is located both in Kuala Lumpur, the capital city of Malaysia and Johor Bahru, the southern city in Iskandar Malaysia, which is a vibrant economic corridor in the south of Peninsular Malaysia
RESEARCH STRUCTURE IN UTM
Three (3) HiCoE
a. Wireless Communication Centre (WCC)
b. Advanced Membrane Technology Centre
c. Institute of Noise and Vibration (IKG)
Six (6) Research Institutes:
a. Ibnu Sina Institute for Scientific and Industrial Research (ISI-SIR)
b. Institute for Smart Infrastructures and Innovative Construction (ISIIC)
c. Institute for Vehicle Systems and Engineering (IVeSE)
d. Institute of Human CenteredEngineering (IHCE)
e. Institute of Future Energy (IFE)
f. Research Institute for Sustainable Environment (RISE)
Five (5) Research Alliances
a. Frontier Materials
b. Innovative Engineering
c. Health and Wellness
d. Resource Sustainability
e. Smart Digital Community
WHO WE ARE
UTM Big Data Centre - rooted from well-experienced researchers and practitionersfrom one of the research groups in UTM –Soft Computing Research Group (SCRG).With almost 20 years experiences in the fieldof Machine Learning, Pattern Recognition,Data Analytics, and Intelligent Graphicsmodelling, as well as in Big Data Analyticsand GPU-based Machine Learning.
PRIMARY FOCUSWhat is our expertise?
Data Science
Big Data
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Big Data Science
UTM BIG DATA PLATFORM 3.0
FUSION
DATA
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GPUMLiB-- AI as a Service
(AIaaS) @ UTM Big Data Centre
GPU MACHINE LEARNING LIBRARY
(GPUMLiB)- OPEN SOURCE LIBRARY
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What we are presenting now
Deep Learning SOM for
Surface Reconstruction
.
The exponential growth of 3D objects, images, devices upto the Nth dimensional representation and constructionwill decrease the performance drastically. Thus, DeepLearning SOM algorithm is proposed in this study tooptimize the performance of 3D reconstruction andrepresentation.
Wai Pai Lee, Shafaatunnur Hasan, Siti Mariyam Shamsuddin and Noel Lopes. GPUMLib: Deep Learning SOM Library for Surface Reconstruction. International Journal of Advances in Soft Computing and its Application, 9, 2(2017), 1-16
What & Why Deep Learning for Surface Reconstruction
Where to implement Deep Learning SOM?
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Surface Representation
Surface Reconstruction
Representing the point cloud data set into a viewable
state in a computer such as a computer vision object.
Including two categories: Explicit and Implicit.
Usually assumed as part of surface reconstruction.
The process of retrieving the point cloud data set
generated by a device.
Connecting the coordinates in point cloud data set which
are attached with the points information.
How to implement Deep-learning SOM in Close Surface Environment ?
How to implement Deep-learning SOM in Close Surface Environment ?
• Point Cloud Collection• PCL data set repositoryPhase 1
• Data Preprocessing• GPUMLib data representationPhase 2
• Deep Learning SOM• Deep Learning GPUMLib SOM Optimization
Phase 3
• Surface Representation• PCL ViewerPhase 4
SOM
SOM
How to implement Deep-learning SOM in Close Surface Environment ?GPUMLib Framework
SOM- GPUMLib RESEARCH DESIGN
GPUMLib Implementation
Host (CPU) and device (GPU) memory access framework Reduction framework
HostArray
DeviceArray
HostMatrix
DeviceMatri
x
CudaArray
…
MinIndex
…
SOM Implementation
Host (CPU) Device (GPU)
Read input data
Initialize weights
Compute distance and find
BMU
Update the weights
Display output
Termination
is satisfied?
Compute distanceComputeDistancesSOMkernel<<<…>>>
Find BMUMinSmallArrayIndex<<<…>>>
Copy value to hostUpdateHost()
Update the weightsUpdateWeightsSOMkernel<<<…>>>
Normalize the weightsNormalizeWeightsSOMkernel<<<…>>>
yes
no
How to implement Deep-learning SOM in Close Surface Environment ?
SOM-GPUMLib Research Design
Point Cloud with
Connectivity
Point Cloud without
Connectivity
Output
point based
on the final
mapping
from the
previous
layer
Output point
based on the
final weights
-Map size reducesas Iteration increases
How is the Architecture of Deep Learning SOM?The Proposed Deep-layer SOM
Original
Output Results
The Proposed Deep-learning SOM Surface Reconstruction Output
Single Layer SOM Deep-Layer SOM
Model Sphere Bunny1 Bunny2 Eagle Sphere Bunny1 Bunny2 Eagle
Points 422 8171 35947 796825 422 8171 35947 796825
Iteration 1000 1000 1000 500 1575 1575 1575 1575
MapX 10 25 25 100 20 40 40 100
MapY 20 40 50 200 40 60 60 200
Time (s) 4.84 124.528 619.12 52560 10.08 60.192 75.21 4392
CPU GPU
Model Sphere Bunny1 Bunny2 Eagle Sphere Bunny1 Bunny2 Eagle
Points 422 8171 35947 796825 422 8171 35947 796825
Iteration 1575 1575 1575 1575 1575 1575 1575 1575
MapX 20 40 40 100 20 40 40 100
MapY 40 60 60 200 40 60 60 200
Time (s) 4.30 64.70 87.32 9540 10.08 60.192 75.21 4392
Performance Comparison between CPU and GPU using Deep-Layer SOM
Deep-layer SOM Performance Comparison between Single Layer SOM and Deep-Layer SOM
Performance Comparison of Single Layer and Deep-layer SOM
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The proposed Deep Learning SOMwill be an alternative solution fordeep optimization in searching, re-organizing and optimizing higherdimensional spaces for complexproblems, i.e., complex design andscenes. Our future work will bedeveloping mobile-based GPUMLib:Deep Learning SOM for optimizingcomplex objects, scenes and related.
AcknowledgementThe authors thank NVIDIA CORPORATION for the support in sponsoring the passes to GTC 2018; Malaysian Ministry of Higher Education (MOHE) for the financial support in conducting this project and Universiti Teknologi Malaysia for the R & D activities.
Conclusion & Future Direction
Short Demo
THANK YOU
&
Terima Kasih
