This document presents a method for 3D representation of noisy point clouds using Growing Self-Organizing Maps (GSOM) accelerated on GPUs. It addresses challenges such as feature extraction, keypoint detection, and noise management in 3D data processing, particularly from low-cost 3D sensors like the Kinect. The proposed approach demonstrates significant improvements in real-time processing capabilities, adaptability to noisy environments, and integration within complex computer vision systems.

1. A Three-Dimensional
Representation method for
Noisy Point Clouds based on
Growing Self-Organizing Maps
accelerated on GPUs
Author:
Sergio Orts Escolano
Supervisors: Dr. José García Rodríguez
Dr. Miguel Ángel Cazorla Quevedo
Doctoral programme in technologies for the information society

2. Outline
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Introduction
3D Representation using Growing Self-Organizing Maps
Improving keypoint detection from noisy 3D
observations
GPGPU parallel implementations
Applications
Conclusions
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3.  Introduction
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•
•
•
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Index
Motivation
Framework
Goals
Proposal
3D representation using growing self-organizing maps
Improving keypoint detection from noisy 3D observations
GPGPU parallel Implementations
Applications
Conclusions
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4. Introduction
Motivation
Motivation

Most computer vision problems require the use of an
effective way of representation
•
•
Graphs, regions of interest (ROI), B-Splines, Octrees,
histograms, …
Key step for later processing stages: feature extraction,
feature matching, classification, keypoint detection, …
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5. Introduction
Motivation
Motivation (II)
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
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
3D data captured from the real world
•
Implicitly is comprised of complex structures and nonlinear models
The advent of low cost 3D sensors
•
•
•
E.g. Microsoft Kinect, Asus Xtion, PrimeSense Carmine, …
RGB and Depth (RGB-D) streams (25 frames per second (fps))
High levels of noise and outliers
Only few works in 3D computer vision deal with real-time
constraints
•
3D data processing algorithms are computationally expensive
Finding a 3D model with different features:
•
•
•
•
Rapid adaptation
Good quality of representation (topology preservation)
Flexibility (non-stationary data)
Robust to noisy data and outliers
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6. Introduction

Motivation
Motivation (III)
3D models of objects and scenes have been extensively used in
computer graphics
•
•
•
Suitable structure for rendering and display
Common graphics representations include quadric surfaces [Gotardo et
al., 2004], B-spline surfaces [Gregorski et al., 2000], and subdivision
surfaces
Not general enough to handle such a variety of features: flexibility,
adaption, noise-aware, … that are present in computer vision problems
(Left) Point cloud captured from a manufactured object (builder helmet).
(Right) 3D mesh generated from the captured point cloud (post-processed)
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7. Introduction
Framework
Framework

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Regional research project (GV/2011/034)
•
Title: “Visual surveillance systems for the identification and
characterization of anomalous behaviour” Project financed by the
Valencian Government in Spain
Regional research project (GRE09-16)
•
Title: “Visual surveillance systems for the identification and
characterization of anomalous behaviour in restricted environments
under temporal constraints” Project financed by the University of
Alicante in Spain
National research project (DPI2009-07144)
•
Title: “Cooperative Simultaneous Localization and Mapping (SLAM) in
large scale environments”
Research stay at IPAB – University of Edinburgh (BEFPI/2012/056)
•
Title: “Real-time 3D feature estimation and keypoint detection of
scenes using GPGPUs”
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8. Introduction
Goals
Goals
 Proposal and validation of a 3D representation
model and a data fitting algorithm for noisy
point clouds
•
•
•
•
Deals with noisy data and outliers
Flexible
Dynamic (non stationary data)
Topology preserving
 An accelerated hardware implementation of the
proposed technique
•
•
Considerable speed-up regard CPU implementations
Real-time frame rates
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9. Introduction
Goals
Goals (II)
 Validation of the proposed method on different
real computer vision problems handling 3D data:
•
•
•
Robot vision: 6DoF Egomotion
3D Object recognition
Computed-aided design/manufacturing (CAD/CAM)
 Integration of 3D data processing algorithms in
complex computer vision systems
•
•
Filtering, downsampling, normal estimation, feature
extraction, keypoint detection, matching, …
The use of a GPU as a general purpose processor
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10. Introduction
Proposal
Proposal
 Growing Self-Organizing Maps (GSOM) for 3D
data representation
•
•
•
Low cost 3D sensors: noisy data
Time-constrained conditions
Applications: 3D computer vision problems
 Hardware-based implementation of the
proposed GSOM method
•
General Purpose computing on Graphics Processing
Units (GPGPU) paradigm
 Integrate the entire pipeline of 3D computer
vision systems using GPGPU paradigm
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11. Index

Introduction
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3D Representation using Growing Self-Organizing Maps
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•
•
•
•
Review
3D Growing Neural Gas network
Experiments: Input space adaptation & normal estimation
Extensions of the GNG algorithm
Improving keypoint detection from noisy 3D observations
GPGPU Parallel Implementations
Applications
Conclusions
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12. 3D Representation GSOM
Review
Review

SOMs were originally proposed for data clustering and pattern
recognition purposes [Kohonen, 1982, Vesanto and Alhoniemi, 2000,
Dittenbach et al., 2001]


As the original model had some drawbacks due to the preestablished topology of the network, growing approaches were
proposed in order to deal with this problem
Growing Neural Gas network has been successfully applied to the
representation of 2D shapes in many computer vision problems
[Stergiopoulou and Papamarkos, 2006, García-Rodríguez et al., 2010,
Baena et al., 2013]

Already exist approaches that use traditional SOMs for 3D data
representation: [Yu, 1999, Junior et al., 2004]
•
•
•
Difficulties to correctly approximate concave structures
High computational cost
Synthetic data
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13. 3D Representation GSOM
Review
Review (II)

Moreover, there exist some limitations and unexplored
topics in the application of SOM-based methods to 3D
representation:
•
•
•
•
•
Majority of these works do not consider the high
computational cost of the learning step
Do not guarantee response within strict time
constraints
Assumed perfect point clouds that were noise-free
Data fusion (Geometric information + colour
information) has not been considered
Not dealing with point cloud sequences, only singleshot data
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14. 3D Representation GSOM
GNG network
3D Growing Neural Gas Network



Obtaining a reduced and compact representation of 3D data
•
Self Organizing Maps – Growing Neural Gas
Growing Neural Gas Algorithm (GNG) [Fritzke, 1995]
•
•
•
•
Incremental training algorithm
Links between the units in the network are established through Competitive
Hebbian Learning (CHL)
Topology Preserving Graph
Flexibility, growth, rapid adaption and good quality of representation.
GNG representation is comprised of nodes (neurons) and connections
(edges)
•
Wire-frame model
Initial, intermediate and final states of the GNG learning algorithm
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15. 3D Representation GSOM
GNG algorithm
GNG algorithm

Input data is defined in ℝ 𝒅
•

Adaption: Reconfiguration module
•

For 3D representation d=3
Random patterns are presented to
the network
Growth: It starts with two neurons
and new neurons are inserted

Flexibility: neurons and
connections may be removed during
the learning stage

This process is repeated until an
ending condition is fulfilled:
•
•
Number of neurons/patterns
Adaptation error threshold
Highly Parallelizable
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16. 3D Representation GSOM
Experiments: Data
Experiments: Data acquisition


Algorithm independent of the data source
We managed 3D data that can come from different sensors
•
Laser unit, a LMS-200 Sick mounted on a sweeping unit.
o Outdoor environments. Its range is 80 metres with an error of 1
millimetre per metre
•
Time-of-Flight camera
o SR4000 camera. It has a range of 5-10 metres
o The accuracy varies depending on the characteristics of the observed
scene, such as objects reflectivity and ambient lighting conditions
o Generation of point clouds during real time acquisition
•
Range camera: structured light, Microsot Kinect device
o RGB-D information. Indoor environments. Its range is from 0.8 to 6
metres
o Generation of point clouds during real time acquisition
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17. 3D Representation GSOM
Experiments: Data
Experiments: Data acquisition
3D sensors used for experiments. From left to right: Sick laser unit
LMS-200, Time-Of-Flight SR4000 camera and Microsoft Kinect
Mobile robots used for experiments.
Left: Magellan Pro unit used
for indoors.
Right: PowerBot used for outdoors.
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18. 3D Representation GSOM
Experiments: Data
Experiments: Data Sets
 Some public data sets have been used to validate
the proposed method:
•
•
•
Well known Stanford 3D scanning repository. It
contains complete models that have been previously
processed (noise free)
Dataset captured using the Kinect sensor. Released by
the Computer Vision Laboratory of the University of
Bologna [Tombari et al., 2010a]
Own dataset obtained using three previously
mentioned 3D sensors
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19. 3D Representation GSOM
Experiments: Data
Experiments: Data Sets

Blensor software: It allowed us to generate synthetic scenes and to
obtain partial views of the generated scene as if a Kinect device was
used
•
It provided us with ground truth information for experiments
Simulated scene
Simulated scene + Gaussian noise
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20. 3D Representation GSOM
Experiments
Experiments
 GNG method has been applied to 3D data
representation
•
•
Input space adaptation
Noise removal properties
 Extensions of the GNG based algorithm
•
•
•
Colour-GNG
Sequences management
3D Surface reconstruction
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21. 3D Representation GSOM
Exp: GNG 3D representation
Experiments: GNG 3D representation
Applying GNG to laser data
Applying GNG to Kinect data
Applying GNG to SR4000 data
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22. 3D Representation GSOM
Exp: GNG 3D representation
Experiments: GNG 3D representation
Applying GNG to Kinect data
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23. 3D Representation GSOM
Exp: Input space Adaptation
Experiments: Input space adaptation

GNG method obtains better adaptation to the input space than
other filtering methods like Voxel Grid technique
• Obtains lower adaptation (Mean Squared Error (MSE) )
• Tested on CAD models and simulated scenes
Lower error
Input space adaptation MSE for different models (metres). Voxel Grid versus
GNG. Numbers in bold provide the best results.
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24. 3D Representation GSOM
Exp: Input space Adaptation
Experiments: Input space adaptation (II)
Noisy model σ = 0.4
GNG representation
Original CAD model
Voxel grid representation
Filtering quality using 10,000 nodes. GNG vs Voxel Grid comparison
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25. 3D Representation GSOM
Exp: Normal estimation
Experiments: Normal estimation


Surface normals are important properties of a geometric surface,
and are heavily used in many areas such as computer vision and
computer graphics
Normal or curvature estimation can be affected by the presence of
noise
Normal estimation over noisy input data

Representation obtained using the GNG method allows to compute
more accurate normal information
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26. 3D Representation GSOM
Exp: Input space Adaptation
Experiments: Normal estimation (II)
Top:. Normal estimation on a filtered point cloud produced by the
GNG method. Bottom: Normal estimation on a raw point cloud.

Normals are considered more stable as their distribution is
smooth and also they have less abrupt changes in their directions
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27. 3D Representation GSOM
Extensions: Colour-GNG
Extension: Colour-GNG
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
Modern 3D sensors provide colour information (e.g.
Kinect, Carmine, Asus Xtion, … )
GNG is extended considering colour information during
the learning step
•
•
•
•
Input data is defined in ℝ 𝒅 where d = 6
Colour information is considered during the weight adaptation
step but it was not included in the CHL (winning neurons)
process
o We are still focus on topology preservation
Winning neuron step only compute Euclidean distance using
x,y,z components
No post-processing steps are required as neurons’ colour is
obtained during the learning process
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28. 3D Representation GSOM
Extensions: Colour-GNG
Extension: Colour-GNG (II)
(a),(b),(c) show original point clouds. (d),(e),(f) show downsampled point
clouds using the proposed method
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29. 3D Representation GSOM
Extensions: Colour-GNG
Extension: Colour-GNG (III)


Mario figure is down-sampled using the Colour-GNG method
Results are similar to those obtained with the colour
interpolation post-processing step
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30. 3D Representation GSOM
Extensions: Sequences
Extension: Sequences management
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Extension of the GNG for
processing sequences of
point clouds
It is not required to restart
the learning
It provides a speed-up in
the runtime as neurons are
kept between point clouds
This extension was
applied in a mobile
robotics application
An improved workflow to manage point
cloud sequences using the GNG algorithm
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31. 3D Representation GSOM
Extension: 3D Reconstruction
Extension: 3D Surface Reconstruction


Three-dimensional surface reconstruction is not
considered in the original GNG algorithm as it only
generates wire-frame models
[Holdstein and Fischer, 2008, Do Rego et al., 2010,
Barhak, 2002] have already considered the creation of 3D
triangular faces modifying the original GNG algorithm
•

Post-processing steps are required for avoid gaps and holes in
the final mesh
We extended the CHL developing a method able to
produce full 3D meshes during the learning stage
•
•
No post-processing steps are required
A new learning scheme was developed
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32. 3D Representation GSOM
Extension: 3D Reconstruction
Extension: 3D Surface Reconstruction (II)

Avoid non-manifold and
overlapping edges
•
•
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More than 2 neighbours
it is checked if the face to
be created already exist
A face is created whenever
the already existing edges
or the new ones form a
triangle
The neuron insertion
process was also modified
Considered situations for edge and face creation
during the extended CHL
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33. 3D Representation GSOM
Extension: 3D Reconstruction
Extension: 3D Surface Reconstruction (III)
Left: The triangle formed by these 3
neurons is close to a right triangle
Right: The edge connecting s1 and ni
is removed as the angle formed
Edge removal constraint based on the Tales sphere
Left: neuron insertion between the
neuron q with highest error and its
neighbour f with highest error.
Right: four new triangles and two
edges are created considering r, q
and f.
Face creation during the insertion of new neurons
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34. 3D Representation GSOM
Extension: 3D Reconstruction
Extension: 3D Surface Reconstruction (IV)
Different views of reconstructed models using an existing GNGbased method [Do Rego et al., 2010] for surface reconstruction

Post-processing steps were avoided causing gaps and holes in
the final 3D reconstructed models
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35. 3D Representation GSOM
Extension: 3D Reconstruction
Extension: 3D Surface Reconstruction (V)
Reconstructed models using our extended GNG method for face
reconstruction and without applying post-processing steps
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36. 3D Representation GSOM
Extension: 3D Reconstruction
Extension: 3D Surface Reconstruction (VI)
Top: 3D model of a person
(Kinect sensor).
Bottom: digitized foot. (foot
digitizer)
Left: Noisy point clouds captured
using the Kinect sensor.
Right: 3D reconstruction using the
proposed method.
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37. Index
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

Introduction
3D Representation using Growing Self-Organizing Maps
Improving keypoint detection from noisy 3D observations
•
•
•
•
Review
Improving keypoint detection
Correspondences matching
Results
GPGPU Parallel Implementations
Applications
Conclusions
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38. Improving Keypoint detection

Review
Review
Filtering and down-sampling have become essential steps in
3D data processing
General System Overview
Motivation: dealing with noisy
data obtained from 3D sensors
as the Microsoft Kinect or lasers

Result: Improving 3D
keypoint detection and
therefore registration
problem
We propose the use of the GNG algorithm for downsampling
and filtering 3D data
 Beneficial
attributes will be demonstrated through the 3D
registration problem
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39. Improving Keypoint detection
Review
Review (II)
Registration: Aligning various 3D point cloud data views into a complete model
Pairwise matching
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40. Improving Keypoint detection
Keypoint detection
3D Keypoint detection
 Applying keypoint detection algorithms to filtered point
clouds
 State-of-the-art 3D keypoint detectors
• Different techniques are used to test and measure the
improvement achieved using GNG method to filter
and downsample input data
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41. Improving Keypoint detection
Keypoint detection
3D Keypoint detection (II)
 3D Keypoint detectors
• SIFT3D:
using depth as the intensity value in the
original SIFT algorithm
• Harris3D: use surface normals of 3D points
• Tomasi3D: performs eigenvalue decomposition over
covariance matrix
• Noble3D: evalutes the ratio between the determinant
and the trace of the covariance matrix
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42. Improving Keypoint detection
Feature descriptors
3D Feature descriptors
 Feature descriptors are calculated over detected keypoints
to perform feature matching
•
•
FPH and FPFH: based on an histogram of the differences of angles
between the normals of the neighbour points
SHOT and CSHOT: a spherical grid centered on the point divides
the neighbourhood so that in each grid bin a weighted histogram
of normals is obtained
FPFH
CSHOT
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43. Improving Keypoint detection
Feature matching
Feature matching (II)
 Correspondences between keypoints are validated
through RANSAC algorithm, rejecting those
inconsistent correspondences
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44. Improving Keypoint detection
Results
Results: Feature matching





Correspondences
matching computed on
different input data
Top: raw point clouds
Middle: reduced
representation using the
GNG (20, 000 neurons)
Bottom: reduced
representation using the
GNG (10, 000 neurons)
RANSAC is used to
reject wrong matches
Raw 3D data
GNG 20,000 nodes
GNG 10,000 nodes
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45. Improving Keypoint detection
Results
Results: Transformation errors
Lowest max errors
Lowest transformation error
*1
Mean, median, minimum and maximum RMS*2 errors of the estimated
transformations using different keypoint detectors. (metres).
*1 Uniform Sampling
*2 Root Mean Square - Transformation error
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46. Index
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

Introduction
3D Representation using Growing Self-Organizing Maps
Improving keypoint detection from noisy 3D observations
GPGPU Parallel Implementations
•
•
•
•
Graphics Processing Unit
GPU-based implementation of the GNG algorithm
GPU-based tensor extraction algorithm
Conclusions
Applications
Conclusions
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47. GPGPU Implementations
GPUs
Graphics Processing Unit
 GPUs have democratized High Performance
Computing (HPC)
• Massively parallel processors on a commodity PC
• Great ratio FLOP/€ compared with other solutions
 However, this is not for free
• New programming model
• Algorithms need to be re-thought and re-implemented
 Growing Neural Gas algorithm is
computationally expensive
• Most computer vision applications are time-constrained
• A GPGPU implementation is proposed
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48. GPGPU Implementations
GPUs
Graphics Processing Unit (II)
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
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
More transistors for data processing
GPU are comprised of streaming multi-processors
High GPU Memory bandwidth
GPGPU: General Purpose computing on Graphics Processors Units
Key hardware feature is that the cores are SIMT
•
Single instruction multiple threads
G80 CUDA NVIDIAs Architecture
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49. GPGPU Implementations
GPU Implementation GNG
GPU Implementation GNG
 Stages of the GNG algorithm
that are highly parallelizable
•
•
•
•
Calculate distance to neurons for
every pattern
Search winning neurons
Delete neurons and edges
Search neuron with max error
 Other improvements
•
•
Avoid memory transfers between
CPU and GPU
Hierarchy of memories
Highly parallelizable
stages
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50. GPGPU Implementations
GPU Implementation GNG
Parallel Min/Max Reduction





A parallel Min/Max reduction
that computes the Min/Max of
large arrays of values (Neurons)
Strategy used to find Min/Max
winning neurons
Reduce linear computational
cost n of the sequential version
to the logarithmic cost log(n)
Provides better performance for
a large number of neurons
Example of Parallel Reduction Algorithm
Proposed version:
2MinParallelReduction
•
Extended version to obtain 2
minimum values in the same
number of steps
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51. GPGPU Implementations
Experimental Setup
Experimental setup
 Main GNG Parameters:
•
•
~0-20,000 neurons and a maximum λ (entries per iteration) of
1,000-2,000
Others parameters have been fixed based on previous works
[García-Rodriguez et al., 2012]
o єw = 0.1 , єn = 0.001
o amax = 250, α = 0.5 , β = 0.0005
 Hardware
• GPUs: CUDA capable devices used in experiments
•
CPU: single thread and multiple thread implementations were
tested
o
Intel Core i3 540 3.07Ghz
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52. GPGPU Implementations
Exp: 2MinParallelReduction
Experiments: 2MinParallelReduction
Runtime and speed-up using CPU and GPU implementations
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53. GPGPU Implementations
Exp: GNG Runtime
Experiments: GNG learning runtime
GPU and CPU GNG runtime, and speed-up for different devices
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54. GPGPU Implementations
Exp: Hybrid version
Experiments: Hybrid version

CPU implementation was faster for small network sizes
•
•
We developed an hybrid implementation
GPU version automatically starts computing when it is detected that
computing time is lower than the one obtained by the CPU
Example of CPU and Hybrid GNG runtime for different devices
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55. GPGPU Implementations
GPU Feature extraction
GPU-based Tensor extraction algorithm
 Time-constrained 3D feature extraction
• Most feature descriptors cannot be computed online due to their
high computational complexity
o 3D Tensor - [Mian et al., 2006b]
o Geometric Histogram - [Hetzel et al., 2001]
•
•
•
Highly parallelizable
Geometrical properties
Invariant to linear
transformations
o Spin Images - [Andrew Johnson, 1997]
 An accelerated GPU-based implementation of an
existing 3D feature extraction algorithm is proposed
•
Accelerate entire pipeline of RGB-D based computer vision
systems
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56. GPGPU Implementations
GPU Feature extraction
GPU-based Tensor extraction algorithm (II)



The surface area of the mesh intersecting each bin of the grid is the value
of the tensor element
As many threads as voxels are launched in parallel where each GPU
thread represent a voxel (bin) of the grid
Each thread computes the area of intersection between the mesh and its
corresponding voxel using Sutherland Hodgman’s polygon clipping
algorithm. [Foley et al., 1990]
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57. GPGPU Implementations
Exp: performance
Experiments: Performance
Runtime comparison and speed-up obtained for proposed methods
using different graphics boards
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58. Index






Introduction
3D Representation using Growing Self-Organizing Maps
Improving keypoint detection from noisy 3D observations
GPGPU Parallel Implementations
Applications
•
•
•
Robotics
Computer Vision
CAD/CAM
Conclusions
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59. Applications
Exp: performance
Applications
 Different cases of study where the GNG-based method
proposed in this PhD thesis was applied to different
areas
•
Robot Vision
o
•
Computer Vision
o
•
6DoF Pose Registration
3D object recognition under cluttered conditions
CAD/CAM
o
Rapid Prototyping in Shoe Last Manufacturing
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60. Applications
Robotics
Robotics: 6DoF pose registration
 The main goal of this application is to perform six degrees
of freedom (6DoF) pose registration in semi-structured
environments
•

We combined our accelerated GNG-based algorithm with
the method proposed in [Viejo and Cazorla, 2013]
•

Man-made indoor and outdoor environments
Planar patches extraction
It provides a good starting point for Simultaneous Location
and Mapping (SLAM)
 GNG was applied directly to raw 3D data
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61. Applications
Robotics
Robotics: 6DoF pose registration (II)
Without GNG


GNG
Left: planar patches extracted from SR4000 camera
Right: filtered data using the GNG network: more planar patches are
extracted
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62. Applications
Robotics
Robotics: 6DoF pose registration (III)
Robot trajectory
Without GNG


GNG
Planar based 6DoF pose registration results
Left image shows map building results without using GNG while the
results shown on the Right are obtained after computing a GNG mesh
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63. Applications
3D Object recognition
3D Object Recognition
 The main goal of this
application is the recognition
of objects under time
constraints and cluttered
conditions
 The GPU-based of the semilocal surface feature (tensor)
is successfully used to
recognize objects in cluttered
scenes
 A library of models is
constructed offline, storing all
extracted 3D tensors in an
efficient way using a hash table
•
Multiple views
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64. Applications
3D Object recognition
3D Object Recognition (II)
 Object recognition is
performed on scenes
with different level of
occlusion
 Objects are occluded by
other objects stored and
non-stored in the library
 The averaged
recognition rate was
84%, wrong matches
16% and false negatives
0%
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65. Applications
3D Object recognition
3D Object Recognition (III)



GPU-based 3D feature implementation is successfully used in a 3D object
recognition application
Parallel matching is performed on the GPU: correlation function
Implemented prototype took around 800 ms with a GPU implementation to
perform 3D object recognition of the entire scene
Scene 2
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66. Applications
CAD: Rapid Prototyping
Rapid Prototyping in Shoe Last Manufacturing
 With
the advent of CAD/CAM and rapid acquisition
devices it is possible to digitize old raised shoe lasts for
reusing them in the shoe last design software
Process to reconstruct existing shoe lasts and computing
topology preservation error regard the original CAD design
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67. Applications
CAD: Rapid Prototyping
Rapid Prototyping in Shoe Last Manufacturing (II)

The main goal of this research is to obtain a grid of points that is
adapted to the topology of the footwear shoe last from a sequence
of sections with disorganized points acquired by sweeping an
optical laser digitizer
Typical sequence of sections of a shoe last.
Noisy data obtained from the digitizer
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68. Applications
CAD: Rapid Prototyping
Rapid Prototyping in Shoe Last Manufacturing (III)
Voxel Grid versus GNG: Mean error
along different sections of the shoe last
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69. Applications
CAD: Rapid Prototyping
Rapid Prototyping in Shoe Last Manufacturing (IV)
Input space
GNG nodes
VG nodes
GNG vs VG topological preservation comparison
3D Reconstruction GNG
3D Reconstruction VG
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70. Index






Introduction
3D Representation using Growing Self-Organizing Maps
Improving keypoint detection from noisy 3D observations
GPGPU Parallel Implementations
Applications
Conclusions
•
•
•
Contributions
Future work
Publications
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71. Conclusions
Contributions
Contributions
 Contributions made in the topic of research:
•
Proposal of a new method to create compact, reduced and
efficient 3D representations from noisy data
‐
Development of a GNG-based method capable to deal with
different sensors
‐
‐
‐
‐
Extension of the GNG algorithm to consider colour information
‐
GPU-based implementation to accelerate the learning process of
the GNG and NG algorithms.
‐
An hybrid implementation of the GNG algorithm that takes
advantage of the CPU and GPU processors
Extension of the GNG algorithm for 3D surface reconstruction
Sequences management
Integration of the proposed method in 3D keypoint detection
algorithms improving their performance
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72. Conclusions
•
Contributions
Contributions (II)
Integration of 3D data processing algorithms in complex
computer vision systems:
‐
‐
Point cloud triangulation has been ported to the GPU
accelerating its runtime
‐
•
Normal estimation has been ported to the GPU considerably
decreasing its runtime
A GPU time-constrained implementation of a 3D feature
extraction algorithm
Application of the proposed method in various real
computer vision applications:
‐
‐
Robotics: Localization and mapping: 6DoF pose registration
Computer vision: 3D object recognition under cluttered
conditionsCAD/CAM: rapid prototyping in shoe last
manufacturing
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73. Conclusions
Future work
Future work
 Other improvements on the GPU implementation of the
GNG algorithm:
•
•
•
•
Using multi-GPU to manage several neural networks
simultaneously
Distributed computing
Testing new architectures: Intel Xeon Phi [Fang et al., 2013a]
Generating random patterns using GPU
 More applications of the accelerated GNG algorithm will be
studied in the future
•
•
Clustering multi-dimensional data: Big Data
Medical Image Reconstruction
 Extension of the real-time implementation of the 3D tensor
•
•
Visual features extracted from RGB information
Improve implicit keypoint detector used by the 3D tensor
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74. Conclusions
Publications
Publications
•
4 JCR Journal papers
o
“Real-time 3D semi-local surface patch extraction using GPGPU”
S. Orts-Escolano, V. Morell, J. Garcia-Rodriguez, M. Cazorla, R.B. Fisher; Journal of
Real-Time Image Processing. December 2013; ISSN: 1861-8219; Impact Factor: 1.156
(JCR 2012)
o
“GPGPU implementation of growing neural gas: Application to 3D
scene reconstruction”
S. Orts, J. García Rodríguez, D. Viejo, M. Cazorla, V. Morell; J.Parallel Distrib. Comput.
72(10); pp: 1361-1372 (2012); ISSN: 0743-7315; ImpactFactor: 1.135 (JCR 2011)
o
“3D-based reconstruction using growing neural gas landmark:
application to rapid prototyping in shoe last manufacturing”
A. Jimeno-Morenilla, J. García-Rodriguez, S. Orts-Escolano, M. Davia-Aracil; The
International Journal of Advanced Manufacturing Technology: May 2013. Vol 69. pp:
657-668; ISSN: 0268-3768; Impact Factor: 1.205 (JCR 2012)
o
“Autonomous Growing Neural Gas for applications with time
constraint: Optimal parameter estimation”
J. García Rodríguez, A. Angelopoulou, J. M. García Chamizo, A. Psarrou, S. OrtsEscolano, V. Morell-Giménez; Neural Networks 32: pp: 196-208 (2012), ISSN: 08936080; Impact Factor: 1.927 (JCR 2012)
74/79

75. Conclusions
Publications
Publications (II)
•
International conferences
o
“Point Light Source Estimation based on Scenes Recorded by a RGB-D
camera”
B. Boom, S. Orts-Escolano, X. Ning, S. McDonagh, P. Sandilands, R.B. Fisher; British
Machine Vision Conference, BMVC 2013, Bristol, UK. Rank B
o
“Point Cloud Data Filtering and Downsampling using Growing Neural
Gas”
S. Orts-Escolano, V. Morell, J. Garcia-Rodriguez and M. Cazorla; International Joint
Conference on Neural Networks, IJCNN 2013, Dallas, Texas. Rank A
o
“Natural User Interfaces in Volume Visualisation Using Microsoft
Kinect”
A. Angelopoulou, J. García Rodríguez, A.Psarrou, M. Mentzelopoulos, B. Reddy, S. OrtsEscolano, J.A. Serra. International Conference on Image Analysis and Processing,
ICIAP2013, Naples, Italy: 11-19. Rank B
o
“Improving Drug Discovery using a neural networks based parallel
scoring functions”
H. Perez-Sanchez, G. D. Guerrero, J. M. Garcia, J. Pena, J. M. Cecilia, G. Cano, S. OrtsEscolano and J. Garcia-Rodriguez. International Joint Conference on Neural Networks,
IJCNN 2013, Dallas, Texas. Rank A
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76. Conclusions
Publications
Publications (III)
•
International conferences
o
“Improving 3D Keypoint Detection from Noisy Data Using Growing
Neural Gas”
J. García Rodríguez, M. Cazorla, S. Orts-Escolano, V. Morell. International Work-Conference
on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain: 480-487.
Rank B
o
“3D Hand Pose Estimation with Neural Networks”
J. A. Serra, J. García Rodríguez, S. Orts-Escolano, J. M. García Chamizo, A. Angelopoulou, A.
Psarrou, M. Mentzelopoulos, J. Montoyo-Bojo, E. Domínguez. International WorkConference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain:
504-512. Rank B
o
“3D Gesture Recognition with Growing Neural Gas”
J. A. Serra-Perez, J. Garcia-Rodriguez, S. Orts-Escolano, J. M. Garcia-Chamizo, A.
Angelopoulou, A. Psarrou, M. Mentzeopoulos, J. Montoyo Bojo. International Joint
Conference on Neural Networks. IJCNN 2013, Dallas, Texas. Rank A
o
“Multi-GPU based camera network system keeps privacy using
Growing Neural Gas”
S. Orts-Escolano, J. García Rodríguez, V. Morell, J.Azorín López, J. M. García Chamizo.
International Joint Conference on Neural Networks (IJCNN) 2012, Brisbane, Australia, June:
1-8. Rank A
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77. Conclusions
Publications
Publications (IV)
•
International conferences
o
“A study of registration techniques for 6DoF SLAM”
V. Morell, M.Cazorla, D. Viejo, S. Orts-Escolano, J. García Rodríguez. International
Conference of the Catalan Association for Artificial Intelligence, CCIA 2012, University of
Alacant, Spain: 111-120. Rank B
o
“Fast Autonomous Growing Neural Gas”
J. García Rodríguez, A. Angelopoulou, J. M. García Chamizo, A. Psarrou, S. Orts, V. Morell.
International Joint Conference on Neural Networks, IJCNN 2011, San Jose, California:
725-732. Rank A
o
“Fast Image Representation with GPU-Based Growing Neural Gas”
J.García Rodríguez, A. Angelopoulou, V. Morell, S. Orts, A. Psarrou, J. M. García Chamizo.
International Work-Conference on Artificial Neural Networks, IWANN 2011,
Torremolinos-Málaga, Spain: 58-65. Rank B
o
“Video and Image Processing with Self-Organizing Neural Networks”
J. García Rodríguez, E. Domínguez, A. Angelopoulou, A. Psarrou, F. J. Mora-Gimeno, S.
Orts, J. M. García Chamizo. International Work-Conference on Artificial Neural Networks,
IWANN 2011, Torremolinos-Málaga, Spain: 98-104. Rank B
77/79

78. Conclusions
Publications
Publications (V)
•
National conferences
o
“Procesamiento de múltiples flujos de datos con Growing Neural Gas
sobre Multi-GPU”
S. Orts-Escolano, J. García-Rodríguez, V. Morell-Giménez. Jornadas de Paralelismo JP,
Elche, España, 2012
•
Book chapters
o
“A Review of Registration Methods on Mobile Robots”
V. Morell-Gimenez, S. Orts-Escolano, J. García Rodríguez, M. Cazorla, D. Viejo. Robotic
Vision: Technologies for Machine Learning and Vision Applications. IGI GLOBAL
o
“Computer Vision Applications of Self-Organizing Neural Networks”
J. García-Rodríguez, J. M. García-Chamizo, S. Orts-Escolano, V. Morell-Gimenez,
J.Serra-Perez, A. Angelolopoulou, M. Cazorla, D. Viejo. Robotic Vision: Technologies
for Machine Learning and Vision Applications. IGI GLOBAL
•
Poster presentations
o
“6DoF pose estimation using Growing Neural Gas Network”
S.Orts, J. Garcia-Rodriguez, D. Viejo, M. Cazorla, V. Morell, J. Serra. 5th International
Conference on Cognitive Systems, Cogsys 2012, TU Vienna, Austria
o
“GPU Accelerated Growing Neural Gas Network”
S. Orts, J. Garcia, V.Morell. Programming and Tuning Massively Parallel Systems, PUMPS
2011,Barcelona, Spain. (Honorable Mention by NVIDIA)
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79. This presentation is licensed under a Creative Commons AttributionNonCommercial-ShareAlike 4.0 International License.
79/79

80. A Three-Dimensional
Representation method for
Noisy Point Clouds based on
Growing Self-Organizing Maps
accelerated on GPUs
Author:
Sergio Orts Escolano
Supervisors: Dr. José García Rodríguez
Dr. Miguel Ángel Cazorla Quevedo
Doctoral programme in technologies for the information society