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Scanners, Digital Cameras and 3D ModelsPart 2

The University of Texas at DallasFriday, ,October 8, 2004

Carlos Aiken, Xueming Xu, and Bobbie Neubert

Lessons Learned• Laser Mapping provides an efficient way to map

the surface geometry, but …• Can capture everything by

– Laser scanning– Photorealistic modeling

• These can be preferable– Requirements/interpretations change.– Need to be able to extract information in the

office

Objectives• Capture everything in three-dimensions • Take it all back to office

– Additional 3D mapping and measurements made back in office

• Render features photorealistically in three-dimensions– Close-range– obliquely

• RESULT: a 3D Virtual World• Standard methods are for perpendicular angles

– not valid for close range/oblique angles

Comparison with 3D colored cloud

• POINT CLOUD IS MADE OF POINTS– AS ZOOM IN SEE LESS DETAIL

• 3D photorealistic model– As zoom in see more detail (dependent only on

resolution of camera).– Can have several photos with different pixel

densities for same 3D surface model.

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Building Photorealistic Virtual Models

• Objectives• Previous Work• Image Registration• Test Case

Traditional Image Registration

• Apply the low order polynomial function to map the image coordinates into world space.

• Only appropriate with a perpendicular perspective and relatively small relief

• Poor accuracy

The georeferenceddigital geological map

The DEM

Geological map draped onto terrain surface

Traditional Image RegistrationGeometric Measurement

• Rely on stereo image sequences.• Require a large amount of correspondence

points.• Time consuming.• Not good for natural surfaces.

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Stereo Image Sequence Capture System

A NEW METHOD FOR MODEL BUILDING

Post-processing, Modeling and Software

Different Sensors

• Scanners • Local coordinate system

• Cameras• Local camera coordinate system

• GPS• Global coordinate system

Coordinate Systems

• Individual local scanner coordinates (each scan)

• Object coordinate system (single coordinate system aligning all scans)

• Camera coordinate system (each photograph)

• Global coordinates

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Scanner Coordinate

• Individual scanner local coordinate

– Not necessary to levelY

X

Z Y

X

Z

Camera Coordinate SystemEach photographhas its ownCoordinatesUnits: mm or

pixel

Putting it together

• From individual scan coordinates to object coordinates

• From object (or global) coordinates to camera coordinates

• From object coordinates to global coordinates

Individual coordinates to object coordinates (1/2)

• Traditional survey approaches– Need to set up a few targets (4-6), then using 7-

parameters transformation to align the scan.

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From individual coordinates to object coordinates (2/2)

• Use mesh alignment techniques (Polyworks type high density scanner software)– No need to level.– Requires overlap with common

features to minimize the distance.Z

X

Ysc1 sc2

T =

From Object to Camera (1/2)

• Two approaches– Polynomial fit (rubber sheeting)

• Low accuracy,• No need to know camera intrinsic parameters

– Projection transform (pinhole model)• High accuracy

From Object to Camera (2/2)1. From object to camera

coordinate system (pin hole model)

2. Perspective projection to convert to image coordinates (uv, pixel, or mm)

6 unknowns assuming known fNonlinear-needs initial value

Camera Calibration

• Correct lens distortion– Radial distortion– Tangential distortion

– Calculate f, k1, k2, p2, p2 in the lab for each lens.

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Example of the calibration (Canon 17mm)

1 2

3

1) Radial distortion2) Tangential distortion3) Complete model

ExampleIteration = 8

Residualspts51 = -0.0027 -0.0065pts50 = 0.0045 0.0085pts2034 = 0.0050 0.0087pts 2010 = -0.0066 -0.0100

omage:0.08839938218814phi:1.36816786714242kappa: 1.45634479894558X: -0.975Y: 0.519Z: -0.013

Bundle Adjustment

Adjust the bundle of light raysto fit each photo

Bundle Adjustment (2/2)Photo no : 7734

pt no U V201 -0.000 -0.000202 0.000 0.003203 -0.000 -0.00614 -0.003 0.01215 0.000 0.001

204 0.001 -0.004205 0.001 -0.009302 0.001 0.004

Photo no omega phi kappa X Y Z7733 3.5147 78.25411 85.03737 -1.031 0.628 0.0467734 21.026 79.86519 68.09084 0.419 14.735 -1.055

Photo no : 7735pt no U V

14 0.003 -0.00615 0.003 -0.001

204 -0.001 0.004205 -0.001 0.00916 0.017 0.005

206 0.000 0.001207 -0.001 -0.009208 0.001 0.010302 -0.006 -0.009

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From Object to Global (1/2)

• 7-parameter conformal transformation

s

Where m11 = cos(phi) * cos(kappa);m12 = -cos(phi) * sin(kappa);m13 = sin(phi)m21 = cos(omega) * sin(kappa) + sin(omage) * sin(phi) * cos(kappa);m22 = cos(omage) * cos(kappa) – sin(omega) * sin(phi) * sin(kappa);m23 = -sin(omage) * cos(phi);m31 = sin(omage) * sin(kappa) – cos(omage) * sin(phi) * cos(kappa);m32 = siin(omage) * cos(kappa) + cos(omage) * sin(phi) * sin(kappa);m33 = cos(omage) * cos(phi);and s is scale factor

Transform to Global (2/2)

Object GPSIteration:5scale: 0.998986 (*****)omega: 0.22279535phi: -0.04740587 kappa: 1.45393837X trans: 24.834 Y trans: 11.698Z trans: 2.142

Pt: 1, X -0.012 Y 0.042 Z 0.010Pt: 2, X 0.008 Y -0.004 Z -0.012Pt: 3, X 0.011 Y 0.012 Z 0.004Pt: 4, X -0.017 Y -0.032 Z -0.007Pt: 5, X 0.010 Y -0.018 Z 0.006

Surface Generation

• Through merge process in software such as Polyworks

• Through surface fitting through CAD software such as GoCad

• Through direct triangulation (Delauneytriangulation, TIN)

Surface cleaning (in Polyworks)

• The single most time consuming part of entire process (90% of time).– Filling the holes

(because of scan shadow)

– Correct triangles

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Summarize ORIGINALTEST:Photorealistic Outcrop at

Dallas Post Office• Accuracy of a few centimeters• Photo registration about 0.7 – 2.7 pixels.• Bring outcrop back to office

photorealistically in three-dimensions• Directly taking measurement on photos in

three-dimensions• Virtual field trip

Dallas Photorealistic Outcrop

Dallas Photorealistic Outcrop Perspective View of Surveyed Points

Using GPS and total station mapped layers (yellow), faults (red) and Terrain (white)

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Terrain Surface(Used GoCad) Initial Test

1) Several natural marks identified on the photo, and surveyed using laser

2) Transformation established using non-linear iterative method

3) Those points back projected into photo-space

Initial Test ResultPhoto Reflector F (mm) Std.

Error(Pixel)

K1 K2 P1 P2

Photo1 No 23.11 2.43 2.88e-5 -5.25e-7 -4.03e-4 4.73e-4

Photo2 No 23.87 2.41 -2.17e-4 -2.80e-7 1.60e-4 5.62e-4

Photo3 Yes 23.59 1.45 -5.54e-4 1.55e-6 -3.92e-5 -4.99e-4

Photo4 Yes 23.18 0.63 -5.23e-4 1.17e-4 2.35e-6 -1.52e-4

Photo5 Yes 23.19 0.72 -5.5e-4 1.46e-6 -5.87e-5 1.45e-4

Photo Mosaic

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Comparison with Direct Laser Tracing

1) Pink and blue points are bedding surface and fault traced by laser

2) Green and blue points are the control points for photograph for transformation

3) Points of bedding and faults back projected to photo space

Re-Digitized Key Beds and Faults

Photorealistic OutcropPhotorealistic Outcrop

Dr. Xueming Xu reinterprets the Austin Chalk at Norsk Hydro

Digital 3D outcrop data may imported into an immersive visualization environment.

New Cybermapping System

• Laser rangefinder/Scanner: • Acquire digital terrain surface• High-end and low-end cost (determined by required accuracy,

resolution , and speed)• NOTE: CAN USE LOW END OR HIGH DENSITY FAST

SCANNERS

• GPS• RTK system: provides global correlation for the laser offsets

• Digital Camera– Determined by interchangeable lens and CCD size

• Consumer-based (non interchangeable, small CCD size, large lens distortion)

• Professional-based

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INITIALLY WILL SHOW GEOLOGIC CASE HISTORIES

• These are natural surfaces• Can demonstrate extraction of 3D

information – from scanning – from 3D photorealistic models

– THEN SHOW OTHER APPLICATIONS

Reflectorless Robotic SystemMDL Quarryman (not a fast scanner)

One point per second

Digital mapping without fast scannersHow photorealistic acquisition works!

Satellite Signal

Radio signal

GPS roverResolution cm

Robotic Scanner

Total station

Scanningoutcrop

outcrop

Satellite Signal

Local GPS baseStation

CORSStations

GPR line

Digital Camera

Equipment used:•Leica 500 RTK GPS system;•MDL Laser Atlanta System;•MDL Laser ACE•Topcon GPT 1002;•Camera Fuji S1pro.

Each photo and scan needs 5+ control pts

Laser-scan of quarry

Jackfork Sandstone, Bigrock Quarry, Arkansas

3D Photorealistic data for interpretation of deep water facies.

Face-On view of outcrop

Courtesy of Veritas

Used MDL Quarryman

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Jackfork Sandstone, Bigrock Quarry, Arkansas

3D Photorealistic data for interpretation of deep water facies.

Face-On view of outcrop

Courtesy of Veritas

Jackfork Sandstone, Bigrock Quarry, Arkansas

3D Photorealistic data for interpretation of deep water facies.

Courtesy of Veritas

Digital Terrain

Jackfork Sandstone, Bigrock Quarry, Arkansas

3D Photorealistic data for interpretation of deep water facies.

Courtesy of Veritas

Digital TerrainDigital TerrainPerspective View

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A. Three-dimensional model of the Big Rock Quarry outcrop. B. Three-dimensional view of a channel connecting the two sides of the quarry.

Most paleocurrents suggest a SW flow direction; few of them have SE markers. However in most cases it was only possible to tell the direction of the flow and not its sense.

C. Channel boundaries highlighted on the 3D model allow reconstruction of submarine channelcomplex architecture.

Big Rock Quarry

Channel

Big Rock Quarry model and interpretation

Ainsa Outcrops, Spain (with Norsk Hydro)

• Ainsa Turbidite System• Four outcrops have been mapped

• Main face: 21 photos, about 1.2 km long and 70 m high

• Canyon outcrop: 9 photos, about 300 m long and 40 m high

• Quarry outcrop: 5 photos, 250 m long and 20 m high

• Road outcrop: 1 photo, 90 m long, 35 m high

• Most of photos have been shot from a helicopter, using 50 mm lens at 3040X2016 resolution

• Models will be used for both training (virtual field trips) and for generating reservoir models of turbidite channel geometries.

Spain

Ainsa, Spain• Cooperative project with Norsk Hydro A/S

– Ole Martinsen, Tore Loseth• Integrated data set

– Photorealistic Models– Satellite/Aerial Imagery– Integration with other consortia data

• Reservoir model constructed

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Ainsa Location

after Cronin et al., 2002after Cronin et al., 2002

Canyon

Main cliff

Quarry

Ainsa Location

Ainsa, Spain Virtual Model Ainsa data integrated with available airborne imagery and DEM

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Applications of 3D Outcrop DataJohn ThurmondImmersive Environment (CAVE)

Courtesy Norsk Hydro

Mineral Wells, Texas

•Syn-sedimentary growth (listric) faults –important in

petroleum and engineering geology

•The mapped outcrop is about 260 m long and 30 m

high

•Camera: original Fuji S1 Pro (3040X2016 at 24 mm lens)

Next Canon D60 (6mb pixels)

"Growth Faulting and Depositional Environments of the Pennsylvanian“Association of Engineering Geologists

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Mineral Well OutcropNote the detail - yet the terrain interval is several decimeters—the photo brings in millions of pixels in detail.

Mineral Well OutcropNote the detail - yet the terrain interval is several decimeters—the photo brings in millions of pixels in detail.

Mineral Well OutcropNote the detail - yet the terrain interval is several decimeters—the photo brings in millions of pixels in detail.

Mickey Hot Springs, Oregonzoom of DOQ

470M

DETAILED

AREA

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Merged surface fit Photos of detail area where 7 photos were mapped onto surface

Oblique perspective of triangulated surface of DA 3D Photorealistic model (7 photos)

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Rotated model Photorealistic model

Vertical perspective—mimicking airphoto

Photorealistic modelNote pixel stretching in green area

Oblique photos cause stretching of pixels.

Meant to use helicopter for more vertical photos.

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Mt. Rushmore, South Dakota

• Mapped like tourists with big equipmentscanning from 3 locations in visitor area-two half daysThree photos over the 2 days10 million points, 2-3cm accuracy and resolution with LPM 3800 Riegl

Will return to add more data from top of heads

One of Photographs

Riegl scanners at Mt. Rushmore

Z360

Riegl LPM 3800

Entire model—merged scans

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Color coded by range Surface fit to scanned points

Final photorealistic model Zoom into and change in perspective

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Cretaceous Ferron Delta, Utah 3D Virtual Outcrops, Integration, and Analysis

A. Example of single laser scan (tone = intensity of return)B. 3D photorealistic Model (surface modeling from laser point

cloud and draped oblique photography)C. Integration with surface geology and GPR cube. D. Data acquisition (Laser Scanning, digital photography, GPS)

Integrating multiple sensors, such as laser scanners, GPS, and multi-spectral cameras, to create a 3D virtual world, which can be visualized, quantitatively analyzed, and integrated with other data sources.

Integration with Geophysical Datarotated partial outcrop

Shorter Range scanner- 100 m - 24 KHz- ~ 1.2 cm Accuracy

Using fast scanner at Salina tunnel, Utah and Mt. Rushmore, South Dakota

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Z360 in action in Salina tunnel. Salina Tunnel, Utah(note fault)

Photorealistic Data Interpretation• Can span a variety of scales

– Small scale single outcrop– Complex canyon systems

• Quality depends on “three dimensionality” of outcrops

• Fundamental difference – data can be reinterpretedany time.

The Power of Surfaces

• Correlation in geology– Do the surfaces match?

• Orientation in geology– Strike and Dip– Fault/Fold orientations– Fault offset/rotation

• Paleogeomorphology

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Integration with other data Norsk Hydro Cave visualization of Ainsa quarry at north end

Preston Canyon, Austin Chalk, Plano

• Initial proof of concept of digital mapping.– Integration of GPS, lasers and portable

computers.• Digital terrain by GPS and lasers.• Real time strike and dip determination.• Initial test of oblique photography.

Looking west just east of Preston Road, Plano, Texas

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Looking east along cut with draped orthoquad and laser mapped geology..

Failed attempt to drape oblique photo onto terrain by rubbersheeting with ESRI software. Orthophoto draped onto terrain.

Triangulated mesh of merged Z360 scans Photorealistic model

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South and north sidesSouth side zoom

North side zooms

Zoom of fault

Viewed from above

Viewed from below

3D Digitization

Digitized layer by GoCad

Surface fit to digitized interface

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Summary• Photorealistic data is best option to ‘take the

outcrop home’

• Instead of mapping individual surfaces, all surfaces can be captured simultaneously

• Multiple interpretations can be made of one photorealistic example

• Integration of other data sets can be a powerful tool for subsurface-to-surface interpretation.

Mineral Wells (Dobbs Valley)

• Association of Engineering Geologists requested a virtual model be built of the Dobbs Valley growth fault outcrops.

• Wanted to take 3D model of outcrops into field on field trip so they could “fly” around the outcrop while in the field.

• UTD acquired data from across the Brazos River of dip and strike sections at 100-200 meters with LPM 3800 in one day.

• GoCAD models of these integrated with Steele’s geologic mapping.

Lecture: Overview of Field Trip:"Growth Faulting and Depositional Environments of the

Pennsylvanian"

Photos from AEG of outcrops from across river

Strike section

Dip section

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Example of photomosaic interpretations from McLinjoy.

Dobbs Valley Growth fault outcrop.Geology by McLinjoy.

Mapping of data onto 3D modelby Erik Brandlin.

Left: entire outcrop.

Lower: with McLinjoy data

Digitized McLinjoy data boundaries (lines) and interpreted surfaces

Mapped features (lines) and analysis (surfacefits

OTHER APPLICATIONS?

• 99.9% of this scanning etc. is applied to man made features– Highways, buildings, monuments, bridges etc.

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The 3D photorealistic mapping of a section of the south bound Dallas Rapid Transit (DART) south of Mockingbird Station, Dallas , Texas,

Riegl Z360 scanner at 8000 to 20,000 points per second. Looking southward, the point cloud from a Reigl Z360 scanner scanning from the center of the track. About 10 aligned scans. The tracks are not of interest only the lining of the tunnel

The triangulated mesh from the point cloud.

The 3D photorealistic model using 14 photos.The blue indicates areas without photos mapped

onto them. The red and white lines are locations of GPR surveys done of the tunnel lining investigating possible problem areas

Close up from the Inside the tunnel. You can see theboundaries of the

photos which with photo qualityprocessing could

be fixed but we do not blend the photos to demonstrate that there is no offset seen across photos in the linear features at the 1-2 cm level

DART railroad tunnelsingle scans

Color coded range scan image

Black/white intensity scan image

DART tunnel mapping with Riegl Z360

Point cloud

Triangulated mesh

DART tunnel photorealistic models

Looking inside tunnel to south

Looking at west side of tunnel