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

[email protected]

303-353-3715

http://www.merrick.com/Geospatial

mailto:[email protected]

http://www.merrick.com/Geospatial