Transcript Photogrammetric Theory - American Surveyor Magazine

3DM Analyst Mine Mapping Suite
Training
Jason Birch, Managing Director
ADAM Technology
[email protected] http://www.adamtech.com.au
Copyright © 2009 ADAM Technology
3DM Analyst
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Presentation Overview
• Principles of Photogrammetry
• Project Types
• Case Studies
• Camera Settings
• Orientations
• Convergent Models
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Principals of Photogrammetry
The location of any point in an image can
be described with just two co-ordinates:
(x,y). Images are only two-dimensional.
The location of any point in the real world
can be described by three co-ordinates:
(x,y,z), (latitude, longitude, altitude), etc.
The real world is three-dimensional.
Photogrammetry is the science of using 2D images to make
accurate measurements in 3D. To do that, the information that was
lost when the image was captured needs to be recovered.
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Principals of Photogrammetry
Problem: The light that hits a given pixel in the image could have
come from any point along the ray from the pixel, through the
perspective centre, into the scene.
Focal
Length
Perspective Centre
Image
Sensor
Possible points of origin
Top-down view
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Principals of Photogrammetry
Solution: Adding another image taken from a different location
allows us to intersect the rays and determine the 3D location of the
point where the light came from!
Image
Sensor
Unique 3D location!
Top-down view
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Principals of Photogrammetry
Information needed to determine 3D locations:
1.
The location of each camera’s perspective centre.
2.
The orientation (rotation) of each camera about its perspective centre.
3.
The location of the point on each image sensor.
Exterior
Orientation
Image Matching
This is the essence of photogrammetry!
3DM Analyst Mine Mapping Suite determines these
completely automatically.
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Important Characteristics of Photogrammetry
• Relatively range-invariant — simply choose the lens required to
obtain the desired ground pixel size:
Focal
Length
Focal
Length
Image
Image
Object
pixelsize
ground

distance
pixelsize
sensor
f
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Important Characteristics of Photogrammetry
• Accuracy highly configurable — planimetric accuracy depends
on image accuracy and ground pixel size; depth accuracy
additionally depends on camera separation:

plan

pixel
 depth 
 pixelsize
ground (previous slide)
distance
 plan
base
Good values for  pixel :
 0.5:
Conservative value for planning
 0.3:
Reasonable estimator of actual accuracy in most cases
 0.05:
Best value actually observed (circular targets)
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Visualising Accuracy
• The relationship between accuracy in the image, planimetric
accuracy, depth accuracy, and base:distance ratio can be
visualised by looking at the “error ellipse” that is formed when we
adjust the location of the point in each image by the image
accuracy:
Error Ellipse
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Visualising Accuracy
• Increasing the base relative to the distance makes the error
ellipse more circular, improving the depth accuracy:
Error Ellipse
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3D Point Accuracy (1-sigma)
200
180
160
140
Canon EOS 5D 50mm (1:3)
Canon EOS 5D 50mm (1:5)
Canon EOS 5D 100mm (1:5)
Canon EOS 5D 200mm (1:5)
Canon EOS 5D 400mm (1:5)
Canon EOS 1Ds Mark II 600mm (1:5)
Canon EOS 1Ds Mark II 1200mm (1:5)
120
mm
3DM Analyst
3DM Analyst
100
80
60
40
20
0
0
100
200
300
400
500
600
700
800
900
metres
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1000
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3D Point Accuracy (1-sigma)
200
180
160
Canon EOS 5D 50mm (1:3)
Canon EOS 5D 50mm (1:5)
Canon EOS 5D 100mm (1:5)
Canon EOS 5D 200mm (1:5)
Canon EOS 5D 400mm (1:5)
Canon EOS 1Ds Mark II 600mm (1:5)
Canon EOS 1Ds Mark II 1200mm (1:5)
Optech ILRUS-3D-ER
Leica ScanStation
Riegl LMS-420i
I-Site 4400LR
140
120
mm
3DM Analyst
3DM Analyst
100
80
60
40
20
0
0
100
200
300
400
500
600
700
800
900
metres
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Limitations of Photogrammetry
• Surface must be textured — image matching doesn’t work on
featureless surfaces
•
Natural surfaces are usually sufficiently textured
•
Pattern projector can be used
•
Targets can be used
• Must be able to see every point of interest from two locations —
“shadowing” effect is twice as bad as a laser scanner
•
Taking additional images from different vantage points to fill in the shadows
doesn’t add much time
• Subject should look similar in each image
•
Change in brightness or colour doesn’t matter; having lots of shadows in
one (e.g. captured late afternoon) and no shadows in the other (e.g.
captured mid-day) hinders matching because shadows look “interesting”
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3DM Analyst Mine Mapping Suite
Digital photogrammetric system designed to work with modern
digital cameras:
• Automatically determines relationships between camera
positions (relative orientation) simply by inspecting images
• Automatically generates 3D surface data from
imagery
• Powerful vector data digitising and editing
tools with 4 billion user-defined
feature types allowed
• Extensive operator assistance whenever human input is required
• Built-in digital camera calibration routines
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• 3DM CalibCam
• Performing camera calibrations (interior orientations)
• Determining exterior orientations (camera positions and directions)
for any number of images simultaneously
• Surveying targets accurately (up to 80,000:1!)
• Creating extremely high-resolution merged images
• 3DM Analyst
• Generating, editing, and merging DTMs
• Digitising vector data using Single Image mode, Stereo, or 3D
mode
• Analysing data — volume calculations, face detection, etc.
• Exporting data as 3D Images, DXF, etc.
• DTM Generator
• Generating DTMs and resulting 3D Images in batch mode
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Workflow
Capture Images
Determine camera orientations
3DM CalibCam
or 3DM Analyst
Generate DTMs & 3D Images (if required)
3DM Analyst or
DTM Generator
Analyse data: calculate volumes, digitise vector data, etc.
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3DM Analyst,
Vulcan,
Surpac, etc.
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Fieldwork Planning
Techniques to ensure:
• That every point of interest is seen from two locations
• That the data can be georeferenced in the desired co-ordinate
system
• Most common: survey at least three locations (control points and/or
camera stations)
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Independent Models
• Pair of images of the same area taken from two different locations
Pros:
• Conceptual simplicity
• Flexibility — depth accuracy can be freely adjusted by changing base,
can be used with lenses of any focal length over any distance
Cons:
• Each model needs to be fully controlled (at least three known locations
— control points and (optionally) camera stations)
• Vulnerable to bad control due to low level of redundancy if very few
control points are used per model due to the total number required
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Image Fans
• Series of images overlapping neighbours by about 10% captured from
each camera station, generally from far away with long focal length lens
First model
Second model
Pros:
• Fewer unknowns give strong orientations with very few control points —
minimum is one control point plus two camera stations or three control
points for entire project, no matter how many images are used!
• Very fast — rotating camera to capture next area much quicker than
relocating camera, allowing large areas to be captured efficiently
• Images can be merged (and treated as independent models in 3DM
Analyst) allowing very high resolution images with cheap cameras
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Image Strips
• Series of parallel images overlapping neighbours by 60%.
(May be arranged in multiple rows, overlapping about 25%.)
Pros:
• Gives accurate exterior orientations with very few control points — one
control point for every five images is generally more than adequate
• Ideal for aerial imagery
Cons:
• Base is determined by field of view, which is determined by focal length
— best with short focal lengths
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Case Studies
The difference between theory and practice is that, in theory, there
isn’t any…
Yogi Berra
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Image Fans, Painted Control
• Camera: Canon EOS 5D, 100 mm lens
• Project area: 390 m × 190 m
• Number of images: 12
• Number of camera stations: 2
• Distance to pit wall: 450 m
• Ground pixel size: ~4 cm
• Number of control points: six, three painted on top bench and
three painted at base of wall
• Accuracy: Sx = 0.04 m, Sy = 0.02 m, Sz = 0.03 m.
• Capturing time: 25 minutes
• Processing time: 15 minutes (4 minutes user time)
• Points generated: 1.5 million
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Image Fans, Painted Control
• Camera: Nikon 1Ds, 135mm lens
• Project area: 500 m × 300 m
• Number of images: 27 from each station
• Number of camera stations: 2
• Distance to pit wall: 700 m
• Ground pixel size: 3 cm
• Number of control points: 7 (around top of the pit)
• Accuracy: Sx = 0.14 m, Sy = 0.08 m, Sz = 0.04 m
• Capturing time: ~ 30 minutes (est.)
• Processing time: 1 hour 38 minutes
(8 minutes user time)
• Points generated: 9.5 million
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Image Fans, Painted Control
• Camera: Canon EOS 5D, 200 mm lens
• Project area: 2300 m × 400 m
• Number of images: 177
• Number of camera stations: 3
• Distance to pit wall: 1200 m
• Ground pixel size: 5 cm
• Number of control points: 50
• Accuracy: Sx = 0.09 m, Sy = 0.11 m, Sz = 0.08 m
• Capturing time: 30 minutes
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Image Strip (car), Painted Control
• Camera: Canon 5D, 28 mm lens
• Project area: 270 m × 70 m
• Number of images: 20
• Distance to pit wall: 30 m
• Ground pixel size: 1 cm
• Number of control points: 3
• Capturing time: < 2 minutes
• Processing time: < 20 minutes
• Points generated: 2 million
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Image Fans, “Natural” Control
• Camera: Canon EOS 5D, 50/100/200 mm lens
• Project area: 400 m × 280 m
• Number of images: 4/13/48
• Number of camera stations: 2
• Distance to pit wall: 600 m
• Ground pixel size: 10/5/2.5 cm
• Number of control points: one (delineator on top of wall) plus two
surveyed camera stations
• Capturing time: 15 minutes (<10 minutes without 200 mm
images)
• Processing time: 6/13/33 minutes
• Points generated: 0.8/2/7 million
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Image Fans, “Natural” Control
• Camera: Canon 5D,
50/100/200 mm lens
• Project area: 200 m × 200 m
• Number of images: 7/17/70
• Number of camera stations: 2
• Distance to pit wall: 85 m (base)
• Ground pixel size: 14/7/3.5 mm
• Number of control points: 10 (natural points, reflectorless TS)
• Capturing time: 10 minutes (all three lenses)
• Processing time: 13/30/128 minutes
• Points generated: 0.7/4.5/16 million points
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No control!
• Camera: Canon 5D, 28 mm lens
• Project area: 200 m × 200 m
• Number of images: 3
• Number of camera stations: 3
• Distance to pit wall: 85 m (base)
• Ground pixel size: 2.5 cm
• Number of control points: 0
(three surveyed camera stations)
• Capturing time: 1 minute 54 seconds
• Processing time: 2 minutes 15 seconds
• Points generated: 255,000
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Aerial Images (Strips), Marked Control (UAV)
• 330 m x 260 m stockpile pad
(8.6 hectares, 21 acres)
• 7 minutes of flying
(30 minutes in the field)
• 30 minutes processing
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Aerial Images (Strips), Marked Control (UAV)
• Over 15 million points
(175 points/m2)
• 20 mm accuracy
• Clear view of entire
stockpile surface
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Aerial Images (Strips), Marked Control (UAV)
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UAV
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Camera Settings
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Camera Settings
Zoom
•
Largest single effect on calibration accuracy — ensure zoom is
consistent with that used for calibration! (Generally means only
minimum and maximum zoom can be used.)
•
Prime lenses are generally cheaper and sharper than zoom lenses
(simpler construction), and cannot accidentally use the wrong zoom.
Focus
•
Second largest effect on the accuracy of a camera calibration —
ensure focal distance is consistent with that used for calibration!
•
For outdoor work, generally focus at infinity and rely on depth of
field to keep scene in focus. (Use the autofocus to focus on an object
distance (cloud, mountain, etc.) to ensure focus is at infinity —
manually setting focus to limit can actually focus “beyond” infinity!)
•
Generally recommend using Auto Focus but beware of focus
changing between images on the same camera station — can use
auto focus for the first image then switch off for the others on the
same station.
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Camera Settings (cont.)
Aperture
•
Small effect on calibration accuracy. Try to ensure aperture remains
consistent or have a range of calibrations for different aperture
settings.
•
Specified in terms of diameter of aperture in relation to focal length, f.
f/8 means diameter of aperture is 1/8th of the focal length.
•
Smaller apertures increase depth of field. Too small and light
refraction blurs image.
•
Recommended range: f/5.6 – f/11.0. Optimal: f/8.0.
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Camera Settings (cont.)
ISO Speed Setting
•
Unlike film, image sensor actually has a constant speed. Higher
speeds simulated by applying a gain, amplifying noise => bad for
image matching. Use the lowest speed setting supported by your
camera, recommended set to ISO 100.
Shutter speed
•
Aperture, ISO Speed Setting, and Shutter Speed control image
exposure. Aperture affects calibration, ISO Speed Setting affects
noise => only Shutter Speed can be used to obtain the correct
exposure.
•
On a bright sunny day, using an aperture of f/8 and ISO 100, correct
exposure time is around 1/200th to 1/250th second — fast enough to
hand-hold the camera. On overcast days, when shutter speed must
be slower than 1/200th of a second, make sure to use a tripod. Also
consider remote shutter release (to reduce movement when
pressing the button) and mirror lockup option on SLRs (to reduce
vibration).
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Taking Images
Lighting
•
Changes in lighting between images causes problems with image
matching. The software can compensate for a great deal of difference
in brightness between images, but changing the light source location
changes the shape of shadows, which can cause difficulties
matching. Do not use the camera’s built-in flash!
Movement
•
If an object moves between images it may be correctly identified and
matched in each image, but because it moved the location will be
wrong.
Regular features
•
Can cause spikes in images if incorrectly matched, especially
horizontal features such as fence rails or scaffolding.
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Orientations
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Terminology: Interior/Exterior
•
Interior Orientation/Camera Calibration — the parameters inside the
camera: focal length, principal point offset, radial lens distortions, etc.
All images in a given project will usually have the same interior
orientation.
•
Exterior Orientation — the parameters outside the camera: position
(x,y,z) and orientation (omega, phi, kappa). Each image in a project will
have a unique exterior orientation.
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Camera Calibration
In the earlier diagrams we assumed that light travels straight through the
lens to the image sensor. This is not the case! The actual image can
deviate considerably from the ideal one.
In addition to determining the precise focal length, 3DM Analyst and
3DM CalibCam correct for the following deviations from an ideal lens:
1.
Principal point offset: Xp and Yp. (Compensates for the optical
centre of the lens not aligning with the centre of the image sensor.)
2.
Radial lens distortions: K1, K2, K3, and K4.
3.
Decentring distortion: P1 and P2. (Compensates for misalignment of
lens elements with each other.)
4.
Scaling factors: B1 and B2. (Compensates for differential scaling in
X and Y and non-perpendicularity of the image axes.)
Typical deviation after calibration: 0.1 to 0.2 pixels!
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Camera Calibration (cont.)
•
In 3DM Analyst, the additional unknowns to be solved means the
minimum number of known 3D locations increases from three (to find
exterior orientations) to at least eight (to perform camera
calibration) — preferably many more! Recommended minimum: 18
points.
•
3DM CalibCam can perform a calibration without control point at all
by using additional images, but adding additional images allows
accuracy estimates to be made. Recommended minimum: 6 images.
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Camera Calibration (cont.)
•
Control configuration is very important. Ensure a good spread of
points in all three dimensions: never allow all known 3D locations to
be colinear (straight line) and do not all them all to be coplanar if
you are doing a camera calibration! The Calibration Report generated
by 3DM CalibCam should be checked before accepting calibration.
•
Ensure there are control points (or relative-only points that appear in
three images if possible) that extend all the way to the edge of the
image.
•
Rotate the camera 90 degrees to capture additional images in portrait
mode to strengthen the calibration.
•
Calibration files can be freely shared between 3DM Analyst and 3DM
CalibCam.
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Camera Calibration (cont.)
Ideal Image geometry for a calilbration project:
2/3
Object distance
Station 1
1/3
Station 2
Station 4
Station 3
Station 5
Station 6
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Terminology: Relative/Absolute
Interior or Exterior Orientations have two sub-categories:
•
Relative Orientation (Free Network) — one that is formed without
using control points or camera stations, usually based on an arbitrary
local co-ordinate system, and usually used when data does not need to
be registered in real-world co-ordinates. Known distances between
camera locations or points in the image (e.g. scale bars) can be used to
obtain correct scaling.
•
Absolute Orientation (Control Network)— one that uses control
points and/or camera stations to register data in a real-world coordinate system.
Both methods can actually be used for registering data in a real-world coordinate system if desired, but it is easier to do so with an absolute
orientation. Relative orientations should be used when the real-world coordinates do not matter and you only need the correct scale.
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Determining Orientations
Given image co-ordinates of a point (x, y) in two images, and knowing
the cameras’ exterior orientations, we can determine the 3D ground coordinates of that point.
Can also work backwards: Given the image co-ordinates of several
points in two images, and knowing the 3D ground co-ordinates of those
points, we can also determine camera’s exterior orientations!
At least three known locations (control points and/or camera stations)
are required for a single project. For 3DM Analyst, this means three per
two images. For 3DM CalibCam, can be any number of images. More is
always better, however.
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How does it work?
The software uses an algorithm called a Least Squares Bundle Block
Adjustment.
“Least Squares”: the solution found is the one that minimises the square
of the error of each observation in terms of their individual sigmas.
“Bundle”: the rays connecting each point in 3D with the associated point
on the image sensor, passing through each camera’s perspective centre,
resembles a bundle.
“Block”: A single row of images is called a strip of images. A project with
multiple rows of images is called a block. 3DM Analyst only solves for
one model (two images) at a time, so “Block” is omitted.
Usually abbreviated to “Bundle Adjustment”.
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Resection
The Bundle Adjustment algorithm can find the optimal solution in a leastsquares sense, but only if it is given an initial solution that is already
approximately correct.
A Resection is used to find that initial approximation.
3DM Analyst implicitly performs a resection before each Bundle
Adjustment.
3DM CalibCam separates the two. A resection should be performed first,
followed by a bundle adjustment. Once a solution has been found, you
can re-do the bundle adjustment (e.g. after adding or deleting points)
without doing the resection again.
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Determining Exterior Orientations
(or Bundle Adjustment)
Inputs:
•
•
•
•
Image co-ordinates of all control points, and Auto Relative only points
Image co-ordinate sigmas (Sx,Sy)
Control point co-ordinates if any
Camera station co-ordinates if any
• Individual co-ordinate sigmas for both control points and camera
stations (Sx,Sy,Sz) if any
Outputs:
•
•
•
•
Adjusted image co-ordinates for all points (RO points)
Adjusted control point co-ordinates if any
Exterior orientations
Derived ground co-ordinates for all of the RO points.
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Summary
•
Photogrammetry determines 3D locations by intersecting rays from
the corresponding pixel in each image through the perspective centre
of each image.
•
Perspective centre locations and orientations (exterior orientations)
are determined by the software by looking at the locations in each
image of corresponding points and working backwards.
•
Camera calibrations (interior orientations) are determined in exactly
the same way, but require more images or control points because
more parameters need to be solved.
•
The easiest way to register data in real-world co-ordinates is to use
control points and/or surveyed camera stations and perform an
absolute orientation.
•
At least three known locations in a triangle shape are required to
register the data for a project. More locations add redundancy and
robustness.
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Convergent Models
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Independent Models
• Pair of images of the same area taken from two different locations
Pros:
• Conceptual simplicity
• Flexibility — depth accuracy can be freely adjusted by changing base,
can be used with lenses of any focal length over any distance
Cons:
• Each model needs to be fully controlled (at least three known locations
— control points and (optionally) camera stations)
• Vulnerable to bad control due to low level of redundancy if very few
control points are used per model due to the total number required
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Step 1: Project Setup (3DM Analyst)
Using the New Project Wizard:
1. Project type: Digital Camera Project.
2. Specify left and right images. (Software will detect later on if
the images are in the wrong order and offer to swap them.)
3. Specify the camera calibration file.
4. Specify the control point file. (Simple ASCII text file containing
both control points and surveyed camera positions, if used.
Note: co-ordinates must be X,Y,Z / East, North, Height, i.e.
right-handed co-ordinate system. Surveyors often give data as
North, East, Height, so first two columns must be swapped.
3DM CalibCam has a button to do this for you.)
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Step 2: Orientations
1. Digitise control points:
•
Circular targets: use target centroiding tool ( ); just click near the
target and the software will find the centre very accurately
(< 0.1 pixels)
•
Other types: use natural point digitising tool ( ). When you have
digitised it in one image, the software will use that to refine the
point you digitise in the other image.
2. Press . The software will search for additional common
points in the images (relative-only points — points for which 3D
locations are unknown) and then perform a bundle adjustment
to determine camera orientations. If successful, the bundle
adjustment report will indicate the quality of the orientations.
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Step 2: Orientations
3. If everything is fine, press OK and the software will proceed to
generate the Digital Terrain Model (DTM/3D Image).
Should be similar
to survey accuracy
Should be similar
to calibration
accuracy (0.1-0.2)
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Step 3: Mapping
1. Click on the 3D View tab at the bottom of the window to switch
to the main view that is used for mapping.
2. Initial view will be a wireframe. Click on the Show Triangles
button ( ) to deactivate it and drape the image over the
surface model.
3. To manipulate the view:
•
Left mouse button + mouse movement = rotation
•
Right mouse button + mouse movement = translation
•
Both mouse buttons + vertical mouse movement = zoom
•
Both mouse buttons + horizontal mouse movement = rotation
4. Note that view manipulation using the mouse is (almost)
always active; to actually digitise points we use a “3D cursor”
rather than the mouse directly.
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Step 3: Mapping — Feature Definitions
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Step 3: Mapping — 3D Cursor
•
The 3D cursor is used for digitising data. To make it follow the
mouse (on the DTM) hold down the Ctrl key or turn it on and off
by pressing the Tab key; at any time, while digitising a feature,
the view can be changed without affecting the feature being
digitised.
•
Press the Centre On Cursor button ( ) to centre the view on
the 3D cursor — useful for checking the flatness of a feature
while digitising it.
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Step 3: Mapping — Digitising Features
1. Press ‘f’ to start digitising a new feature.
2. Press the spacebar to add a point (the current location of the
3D cursor) to the feature.
3. Press ‘s’ to save the feature.
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Step 3: Mapping — Digitising Features
•
The number of points added to a plane feature determines how
it is generated:
•
1 point — the software will attempt to “grow” a plane from the
digitised points using the parameters in the Discontinuity Analysis
dialog and find the least squares bet fit plane for the points it
determines belong to the same face. Only useful for planes.
(Automatic face detection uses the same technique but will detect
all flat surfaces larger than the user-specified size.)
•
2 points — allow you to directly specify the origin and the normal
of the plane.
•
3+ points — the software will find the least squares bet fit plane to
the points you have digitised. Useful for both faces and traces.
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Step 3: Mapping — Feature Info
1. Move the 3D cursor near a feature and press the Enter key to
snap on to it.
2. Press ‘i’ to bring up the Feature Info dialog box.
3. Click on “Measure” and snap on to the next feature in the set to
measure the spacing (and relative angle).
4. Export using Feature Info |
Feature info List. CSV format
is best.
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Image Fans
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Image Fans
• Series of images overlapping neighbours by about 10% captured from
each camera station, generally from far away with long focal length lens
First model
Second model
Pros:
• Fewer unknowns give strong orientations with very few control points —
minimum is one control point plus two camera stations or three control
points for entire project, no matter how many images are used!
• Very fast — rotating camera to capture next area much quicker than
relocating camera, allowing large areas to be captured efficiently
• Images can be merged (and treated as independent models in 3DM
Analyst) allowing very high resolution images with cheap cameras
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Step -1: DTM Generator
1. Double-click on the DTM Generator icon to launch DTM
Generator.
2. Click on the close box in the top-right corner of the window to
exit.
(This step only needs to be completed once after the software
has been installed, just so 3DM CalibCam knows where
DTM Generator is. In the future this step will not be necessary.)
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Step 0: Camera Setup (3DM CalibCam)
1. Right-click on Camera DataBase, select Add New Camera.
2. Click Read...
3. Open the .cal file for the camera to be used.
(This step is usually not required because the camera will
already be in your database.)
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Step 1: Project Setup (3DM CalibCam)
1. Left-click on the camera in the Camera DataBase and drag it
onto New Project.
2. Right-click on the camera, select Add Image.
3. Select all of the images in the project and click Open.
4. Right-click DataSet, select Add Data Set. Click on Browse to
specify the control point file, then specify the units and click
OK. (Simple ASCII text file containing both control points and
surveyed camera positions, if used. Note: co-ordinates must be
X,Y,Z / East, North, Height, i.e. right-handed co-ordinate
system. Surveyors often give data as North, East, Height, so
first two columns must be swapped. To do this, double-click on
the control point file under Data Set and click Swap XY.)
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Step 1: Project Setup (Cont.)
5. Right-click on the camera and select Add Camera Station.
6. Under Station without Control Point, click Add once for each
camera station.
7. Select the images taken from each station and drag them onto
the appropriate station.
(These steps are only necessary if you have taken more than
one image from each location, or if you have surveyed camera
locations. In the latter case, use Station with Control Point
and choose the point ID that is acting as a camera station from
the list and click Add.)
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Step 2: Relative Orientations
The relative orientations are the orientations (position and
direction) of the cameras in an arbitrary co-ordinate system.
1. Select Digitising Tools | Generate Relative-Only Points and
press Start. (This can take a very long time for large projects
with hundreds of images.)
Relative-only points are common points between images for
which 3D co-ordinates are not provided to the software. The
software uses these to determine the relative orientations.
Because their precise locations are unimportant, the software
can generate these automatically. If it is having difficulty finding
common points, or if there are particular locations in the
images that you wish to determine 3D data for, you can digitise
additional relative-only points by hand.
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Step 2: Relative Orientations (Cont.)
2. Press Close. Select Exterior | Free Network and click on
Resection. The software will attempt to determine the
approximate orientations of the cameras. (Resection is very
clever but not all that accurate — it’s job is to start with no
knowledge about the camera orientations and come up with an
approximate solution that’s good enough for the next step.)
Can be quite large
at this stage;
problems occur
when the values are
100s of pixels or
more
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Step 2: Relative Orientations (Cont.)
3. If the resection is successful, click OK, then Adjust — the
software will now attempt to refine that initial approximation and
produce an accurate solution. If successful, click Close.
Should be similar to calibration
accuracy (0.1-0.3).
If larger it indicates the
presence of bad RO points,
bad calibration, or something
wrong with the images
(images on wrong station,
images changing focus
between images on same
station, etc.)
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Step 2: Relative Orientations (Cont.)
4. Choose Edit | Remove Bad Relative-Only Points and click
OK to have the software search for possible bad points and
remove them.
5. Reselect Exterior | Free Network, Adjust to redo the bundle
adjustment with any bad points removed. (It is not necessary to
redo the resection because the solution from last adjustment
should be fine as an approximate solution this time.)
At this point, if the RMS Pt (Pixel) values are not within the
calibration accuracy, it is worth investigating (starting with the
images with the largest values). If you see lots of points with
tails, and the tails are in a pattern (adjacent points have similar
sized tails in a similar direction) then it indicates a systematic
problem (calibration, wrong camera station, changing focus,
not rotating about the perspective centre). If random, suspect
more bad points — repeat the last two steps.
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Step 3: Absolute Orientations
1. Digitise control points:
•
Circular targets: use target centroiding tool ( ); just click near the
target and the software will find the centre very accurately
(< 0.1 pixels)
•
Other types: use natural point digitising tool (
):
Use sliders to
adjust zoom and
appearance to
make it easier to
digitise accurately
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Step 3: Absolute Orientations (Cont.)
Natural point digitising operation:
i.
Make sure the control point ID in the toolbar is correct:
ii.
Move the mouse to the approximate location and press ‘l’ to
lock the view.
iii.
Use the arrow keys to refine the position. (Press Shift to move
faster.) Make sure the mouse is over the background window
or the arrow keys will move the last slider you touched instead!
iv.
Press Spacebar to digitise the point.
v.
Repeat for a total of three control points in two images each.
The two images for each control point must be captured from
two different locations. (You can have all three points in each
of two images, or one point per image, or any combination
thereof.)
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Step 3: Absolute Orientations (Cont.)
2. Press Close. Select Exterior | Control Network and click on
Resection. The software will attempt to determine the
approximate orientations of the cameras, this time in the
desired real-world co-ordinate system.
Values should be
similar to last
free network
resection.
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Step 3: Absolute Orientations (Cont.)
3. If the resection is successful, click OK, then Adjust — the
software will now attempt to refine that initial approximation and
produce an accurate solution. If successful, click Close.
Not yet reliable, only
three points in use. Will
tend to be very small.
Should be similar to
calibration accuracy
(0.1-0.3).
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Step 3: Absolute Orientations (Cont.)
4.
Digitise remaining control points:
•
Circular targets: visit each image and use driveback tool (
•
Other types: use natural point digitising tool again, but this time
ask it to drive to the approximate location of each point. Hold
down:
).
Shift — to skip over already-digitised points
Ctrl — to only consider control points
and click on Next to go to the next undigitised control point.
Digitise it as before and repeat until all control points digitised —
the software will visit every undigitised control point in every
image in turn.
If the positions that the dialog drives to are still quite far from the
correct locations, after digitising a few additional points try doing
Exterior | Control Network | Adjust again, then continue.
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Step 3: Absolute Orientations (Cont.)
4.
Perform a final absolute adjustment (Exterior | Control
Network | Adjust) and verify the accuracy results are within
the expected range. Save the project.
Should now be within
expected values given
survey accuracy and
expected project accuracy.
If not, view the Control
Points RMS Error Summary
table in the HTML Report
for highlighted control
points to investigate.
Check to view
detailed HTML
report.
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The Trouble Shooting
button on the Exterior
Orientation — Control
Network dialog will also
check the control points for
you and inform you if any
are suspect.
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Step 4: Image Merging
1. Right-click on a camera station and select Create Merged
Images. (Choosing the left camera station will ensure all DTMs
generated later are colour-balanced.)
2. Adjust the area to be included in the merged image to be
created by dragging on the blue boxes in the image preview.
3. Specify the file name (e.g. Station1.jpg, Left.jpg, ...) and click
Add/Update.
4. Click Create and Close when finished.
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Step 5: DTM Generation (Option 1)
(This option allows you to combine a single merged image with
multiple original images from the other station. It has the
advantage that each DTM/project remains small and easy to
manage, while still removing the need for each image to line up
with the corresponding image in the other station.)
•
After making sure the project is saved, right-click on the
same camera station and select Remove Camera Station.
•
Right-click on the project and select Add Image. Select the
merged image just created. If the software warns you that the
point IDs overlap and asks if you want to shift them, select No.
•
Select Report | Image Pair List.
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Step 5: DTM Generation (Option 1)
4. Select Report | Image Pair List.
5. In the Image Pairs Report, click on the checkbox on each row
then click Generate Names.
6. Make sure Launch DTM Generator is selected then click
Create Projects. (Make sure you have run DTM Generator at
least once prior to this, even if you just close it again
afterwards. See Step -1.)
7. Click on Check All to verify all projects are in order, then
Generate DTMs.
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Step 5: DTM Generation (Option 2)
(This option allows you to create two merged images and
process them as a convergent model. It has the advantage that
you only have a single DTM, but be making the merged images
too large can cause memory problems.)
•
Repeat Step 4 for the other camera station, creating an
appropriately-named image (Station2.jpg, Right.jpg, …)
•
Launch 3DM Analyst as per the Convergent Models case, but
do not specify a camera calibration in Step 1 point 3
(specifying a control point file in Step 1 point 4 is optional) and
do not digitise any control points in Step 2 (they will already
be digitised). When you press “GO”, 3DM Analyst will
recognise that the orientations have already been performed
and will skip straight to the DTM generation phase.
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Step 6: Mapping, Etc.
Each project created by DTM Generator can now be loaded directly
into 3DM Analyst for mapping.
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Image Strips
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Image Strips
• Series of parallel images overlapping neighbours by 60%.
(May be arranged in multiple rows, overlapping about 25%.)
Pros:
• Gives accurate exterior orientations with very few control points — one
control point for every five images is generally more than adequate
• Ideal for aerial imagery
Cons:
• Base is determined by field of view, which is determined by focal length
— best with short focal lengths
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Image Strips (3DM CalibCam)
Exactly the same as image fans, except:
•
Skip points 5-7 in Step 1 because there are no camera
stations.
•
Skip Step 4 because there is no image merging.
(Note that you can use a strip of merged images if you wish —
same as image fans, but there are more than two camera
stations.)
3. Follow only steps 3-7 of Step 5 (Option 1) to use DTM
Generator to generate the DTMs in batch mode. Alternatively,
after saving the project in 3DM CalibCam, you can run the New
Project Wizard in 3DM Analyst as per the Convergent Models
case, but do not specify a camera calibration in Step 1 point
3. (Specifying a control point file in Step 1 point 4 is optional.)
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