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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.
2
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.
3
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
4
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.
5
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.
6
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
7
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
8
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)
9
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
10
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
11
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
12
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
13
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
14
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
15
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
16
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
17
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)
18
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.
19
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
20
Color coded by range Surface fit to scanned points
Final photorealistic model Zoom into and change in perspective
21
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
22
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
23
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
24
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
25
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
26
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
27
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.
28
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