Transcript Missie en Visie TU Delft

Features of Point Clouds and
Functionalities of Processing
Software
Mathias J.P.M. Lemmens
Delft University of Technology, The Netherlands (MSc Geomatics for the
Built Environment: (1) Geodata Acquisition Technology & (2) Geodata Quality)
Senior Editor GIM International
International Consultant (2014 -2015: WB, Kenya)
[email protected]
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“It’s very encouraging to
know that the community
is so big.”
GIM International senior editor Mathias Lemmens is guiding his TU Delft
students through Intergeo, Berlin, 2014.
“The first day our professor guided us along a variety of booths and showed
us how to fearlessly shoot questions and ask for demonstrations.”
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Published by Springer, 2011
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Nucleus of Point Clouds
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Shukhov Tower
Moscow, Russia
Built in 1919-1922,
radio broadcasting
Monument of Russian
avant-garde
architecture
100 million points
accuracy: 7mm
Mikhail Anikushkin & Andrey Leonov, Russia, 3D Modelling
of Shukhov Tower3D Modelling of Shukhov Tower, GIM
Int’l, July 2014
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Mobile Mapping System
Terrasolid, Road Maintenance with
MMS, pilot in Finland in 2012, GIM
Int’l, July 2014
Section of 22 km of the two-lane NR6 (Finland) with Trimble MX8:
2 Riegl VQ-250 scanners
4 cameras – 1 recording road surface and 3pointing forward
Applanix POS LV 520
MMS data processed with sophisticated software is well suited
for road maintenance (+ machine steering) tasks as many
parameters can be accurately calculated from the 3D model.
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Airborne Lidar
Netherlands
• 40% below sea-level
• Threats from Sea, Germany (Rhine) and France (Meuse)
• Height Model
• 1998: 1 pnt / 25 square meters
• 2013: 10 pnts / square meter
On Average doubling of number of points every
two years.
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Aerial multi camera systems capture
oblique and nadir imagery at the same
time  full and intuitive view on both
building footprints and facades
beneficial for creating 3D city models.
Object identification and creation of
dense 3D point clouds are easier and
more reliable compared to conventional
vertical imagery although the cost of
capturing is higher.
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Dense image matching allows point densities similar to
the ground sampling distance (GSD) of the imagery from
which they are derived. (e.g. GSD of 10cm  100 height
points per square meter.
Semi-global matching (SGM) algorithm introduced by
Hirschmüller (2008)
Challenging for oblique imagery:
- large scale variations
- illumination changes
- many occlusions
Need for performance measures of DIM software
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M. Deuber, S. Cavegn, S. Nebiker (Switzerland) Dense
Image Matching – Performance Analysis on Oblique
Imagery, GIM Int’l, Sept. 2014.
Task
Photo-
Stereo-
Scan
SGBM
Image rectification
V
Image matching
V
Point cloud generation
DSM computation
DSM texturing
SURE
Xpro
SGM
V
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V
V
V
V
V
V
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Completeness
Ratio between number of pixels to which software
assigns a depth value and total number of pixels.
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Massive Point Clouds Everywhere
What to do with all those points?  Benchmark
EuroSDR Survey - Oblique Airborne Photogrammetry: Users’
and Vendor’s View
Markus Gerke, University of Twente, The Netherlands
Fabio Remondino, FBK Trento, Italy
(To be published in GIM Int’l Dec. 2014)
Massive Point clouds for eSciences; Delft University of
Technology; Rijkswaterstaat; Netherlands eScience Centre;
Fugro and Oracle
(Submitted to Computers & Graphics)
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EuroSDR
Survey
130 Respondents; Universities 45%, National
Mapping Agencies 21%
Questionnaire still open
https://www.surveymonkey.com/s/EuroSDR_oblique
What can obliques do better?
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Massive Point Clouds for eSciences
• Point clouds are too massive for efficient handling by
common Geo-ICT infrastructures
• User requirements by structured interviews with users.
• Based on requirements a benchmark has been designed
by comparing the loading and querying of data sets
consisting of 20 million, 20 billion and 640 billion points
• Oracle, PostgreSQL, MonetDB and LAStools.
• Proposals for storage improvement and thus
accessibility
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Sources of Point Clouds
• Millions, billions, trillions of 3D points
• Airborne Lidar
• Overlapping imagery (Dense Image Matching)
• Terrestrial Laser Scanning / Mobile Mapping
• Spaceborne and Airborne Radar (e.g. Terrasat)
• Sonar
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Functionalities
Of Point Cloud
Processing
software
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Software
General purpose: handle point clouds from a diversity of
sensors (e.g. Terrasolid)
Dedicated to specific output e.g. TLS, airborne Lidar, MMS or
sonar (proprietary software)
Focus may be on a broad pallet of end products from a
particular sensor type
Other end of the spectrum: application domains.
Constructor using CAD exploits TLS point clouds and want
modules for processing them.
Dedicated modules on top of base modules, e.g. mining
industry or 3D models of crash sites.
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Development far from complete
New tools are being added all the time
A generic package for all types of sensor outputs end
products does not exist
Look at functionalities but also examine design ideas,
current or planned extensions, its ability to join modules
into one workflow, and interoperability with other
software and services.
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To compare Point Cloud Processing Software have a
look at the successor of GIM’s Product Surveys
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Point Clouds –
Acquisition, Processing
and Management
to be published by
Whittless Publishing, UK, in
2016.
To be presented at ISPRS
Congress, 2016, Prague.
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Thank you so much for your
attention.
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