Real-Time 3D Data Visualization and Analysis of Unlimited Detail

Engineering | Architecture | Design-Build | Surveying | GeoSpatial Solutions
Real-Time 3D Data
Visualization and
Analysis of Unlimited
Detail
March 27, 2013

Copyright © 2010 Merrick & Company All rights reserved.
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Presenter
Josh Beck, GISP
Software Consultant for Merrick & Company

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Corporate headquarters: Greenwood Village, CO
Founded in 1955; employee-owned
Corporate Overview
Geospatial
 LiDAR Acquisition and
Processing
 MARS© LiDAR
Processing Software
 Orthophoto and
Photogrammetric
 MS / HSI / TIR
 Multi-Sensor Data
Fusion
 Satellite Imagery
 Application
Development
 Surveying
 UAS Mapping
National Security
 Dept. of Defense
 Federal
Nuclear
 Commercial Nuclear
 DOE / NNSA
Energy
 Oil & Gas
 Utilities
 Bioprocessing
 Renewables
 Nuclear
 The Merrick
Consultancy
Life Sciences
 Federal
 University / Private
 Commissioning /
Validation
 Healthcare Facilities
Infrastructure
 Water Services
 Local Government
 Private Development
 Federal / DoD
 Energy
 Sustainable
Infrastructure

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Merrick & Company Office Locations
500 employees at 13 national
and 4 international offices

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Merrick’s International Project Experience

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LiDAR Data Management
Traditional Display and Rendering Methods

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Points Loaded into RAM
Pros Cons Conclusion
Potential for fast 2D and
3D point rendering,
depending on data size
Data processing and view
rendering sessions are
limited by RAM –
performance can degrade
even with large amounts
of RAM utilized
Great for small datasets,
commonly used for
creating static videos of
point clouds (must
recreate video from
scratch for alternative
perspectives)
Fast access to data for
processing, depending on
data size
Wait time for points
loading into RAM is
typically slow,
impracticable for massive
LiDAR datasets
Ineffective for very dense
LiDAR data captures, or
extensive geographic
regions. Impossible to
render huge volumes of
point clouds (“Big Data”)
at full resolution

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Points Loaded into RAM Example

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Point Streaming, Low Resolution Block
Technique, and Point Decimation
Special (not typical) point
decimation techniques
can “intelligently” reduce
the data size while
preserving the important
details (i.e. model
keypoint creations)
Data rendering is
decimated during
navigation and then
streamed in when
movement stops, still a
slow rendering processes
as the software fetches
points from disc
This is the current
accepted technique for
rendering large point
clouds. Most geospatial
analysts are trained to
accept the visual
limitations imposed by
software, hardware, and
“Big Data”.
Less RAM is required
than previous technique,
though more RAM does
increase the performance
High risk of missing small
or key features during
navigation due to minimal
point display during
movements
Unsatisfactory for many
applications where
access to all the data at
full resolution should be
mandatory

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Point Streaming, Low Resolution Block
Technique, and Point Decimation Example

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LiDAR Data Management
Breakthrough Technology

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Advanced Spatial Indexing
All points are viewable at
all times, instantly
Initial one-time indexing
or pre-processing can
take time but once it’s
done, the data loads
instantaneously
Best technology for large
point cloud data
management
Minimal RAM utilization
since spatial indexing is
file and software based
Indexing can require high
performance hardware to
process data
Best technology for
massive LiDAR point
cloud rendering

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Unlimited Size and Full 3D Resolution
 Access to all LiDAR and/or 3D point data, no matter what
the size
 The current system has been tested to support approximately 1.4
petabytes of LAS data – this is only a test limit, not a system limit
 The system has been tested with synthetic data at a density of 64
points per cubic millimeter across a test area of 1 square kilometer
 2D and 3D navigation is instantaneous, no waiting for data
to load or streaming of data into view window
 Able to see every point in the dataset, only limited by the
number of screen pixels on the viewing device
 Load times and speed of performance are not related to
the size or resolution of the 3D data
 Load times are less than one second, no matter how large
the datasets

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Additional Advantages
 Points are stored in a compressed format
 Compression ratios are around 5-20% of the original LAS size
 No decompression lag unlike other compressed LiDAR technologies or
file compressors like Winzip or WinRAR
 Data can be viewed in stereoscopic 3D if desired
 This technology is accessible through a converter/viewer or SDK
 No dependency on high end computer hardware
 Utilizes multi-core and/or multi-threaded CPUs but does not require high
end models
 Does not require GP-GPU processing for high speed performance
 Does not require large amounts of RAM, 2 GB is adequate
 Does not even require high speed disc I/O. Rendering speed is similar
whether the data is local, on a network, or even accessed through a
USB 2.0 port

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Size, Load Time, and Computer Resources

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Next Generation 3D Software
 3D data freedom
 Points are not loaded into RAM - no more memory limitation
 Points are not decimated or pyramided – no more thinned data displays
 Real time 3D point access is direct from disc or network utilizing
two revolutionary techniques:
 Advanced 3D spatial index
 Intelligent search algorithm to find and display just the points needed - at
most, one point per screen pixel at 30 frames per second
 Ability to work with the actual 3D data, not derivative products or a
combination of different geospatial data types
 No need to grid elevation data, loosing critical 3D information
 Movement away from 2D or 2.5D - actual points can be viewed and
measured in 3D space, not 3D represented by only colors or shadings
 No need to keep features separated by data types (vector, raster, etc.)

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Urban Example

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Rural Example

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Mobile Example

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Terrestrial Example

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Internet Streaming
 3D models can be accessed worldwide across a network
or the internet
 No need for multiple local storage points of 3D models if an
internet connection is available
 3D models can be hosted at a single location
 Only a single copy of the data needs to be stored which
can be served up to unlimited internet users, dramatically
decreasing data storage requirements, costs, and
complexities
 Version control - if changes to the data are required, these
are done only one time to the source data
 Data can even be streamed over the internet without the
need for a browser plug-in

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Colombian Coast Demo

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More Than Just LiDAR
 3D data can present imagery or originate from it
 RGB fused LiDAR
 3D Ortho – interpolated LiDAR at the resolution of imagery
 Colorized 3D point clouds derived from stereo pairs using dense
matching techniques (i.e. semi-global matching or multi-ray
photogrammetry)
 3D models from multi-angled oblique imagery
 More groundbreaking possibilities in the works
 3D Models
 3D models can automatically be converted to this format to be
rendered and streamed from a single platform in unlimited amounts
and detail

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3D Ortho – not an image drape or TIN render

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Semi-Global Matching
Images courtesy of Intergraph

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Oblique Image SGM
Images courtesy of Intergraph

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Multi-Ray Photogrammetry
Images courtesy of Microsoft's UltraMap 3.0

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Oblique Image Texturing Example

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Summary
 Real time 3D data rendering at full resolution and detail
 Compressed data size
 Data loads in 1 second no matter the size
 Unlimited amount of data to manage, load, display, etc.
 Low computer resource requirements
 Preserves true 3D data, not rasterized derivative products
 Capable of streaming 3D data across network or internet
 Centralized data storage
 Version control
 Big data is now accessible and even portable
 Opportunity for 3D imagery and 3D models

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Thank you
Josh Beck
Software Consultant
Merrick & Company
josh.beck@merrick.com
303-353-3715
http://www.merrick.com/Geospatial

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