Transcript week11-new3D_A.ppt

CV: 3D sensing and calibration
Coordinate system changes;
perspective transformation;
Stereo and structured light
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roadmap
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using multiple cameras
using structured light projector
3D transformations
general perspective transformation
justification of 3x4 camera model
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Four Coordinate frames
W: world,
C,D: cameras,
M: object model
Need to relate all
to each other.
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Can we recognize?
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Is there some object M
That can be placed in
some location
That will create the two
images that are
observed?
Discover/compute what
object and what pose
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Need to relate frames to compute
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relate camera to world using rotations
and translations
project world point into real image
using projection
scale image point in real image plane
to get pixel array coordinates
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Stereo configuration
2 corresponding image
points enable the
intersection of 2 rays in W
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Stereo computation
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Math for stereo computations
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need to calibrate both cameras to W so
that rays in x,y,z reference same space
need to have corresponding points
find point of closest approach of the
two rays
(rays are too far apart  point
correspondence error or crude
calibration)
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Replace camera with projector
Can calibrate a projector to W
easily. Correspondence now
means identifying marks.
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Advantages/disadvantages of
structured light
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Grid projected on objects
All grid
intersects are
integral
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Computing surface normals
Surface normals
have been
computed and
then added to the
image (augmented
reality)
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Relating coordinate frames
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need to relate camera frame to world
need to rotate, translate, and scale
coordinate systems
need to project world points to the
image plane
all the above are modeled using 4x4
matrices and 1x4 points in
homogeneous coordinates
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Translation of 3D point P
Point in
frame 2
Point in 3D
parameters
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Point in
frame 1
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Scaling 3D point P
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Rotation of P about the X-axis
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Rotate P about the Y-axis
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Rotate P about the Z-axis
Looks same as 2D rotation omitting row, col 4
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Arbitrary rotation has
orthonormal rows and columns
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Example: camera relative to world
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exercise
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verify that the 3 x 3 rotation matrix is
orthonormal by checking 6 dot products
invert the 3 x 3 rotation matrix
invert the 4 x 4 matrix
verify that the new matrix transforms
points correctly from C to W
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Transformation “calculus”:
notation accounts for transforms
Destination
frame W
W
M
Origin frame M
T
Denotes
transformation
T transforms points
from model frame to
world frame. (Notation
from John Craig, 1986)
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Apply transformations to points
Point in model
coordinates
W
P =
Point in world
coordinates
W
M
T
M
P
Transformation from model
to world coordinates
(instance transformation)
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Matrix algebra enables composition
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Let M and N be 4 x 4 matrices and let P be a
4 x 1 point
M(NP) = (MN)P
we can transform P using N and then
transform that by M, or we can multiply
matrices M and N and then apply that to
point P
matrix multiplication is associative (but not
commutative)
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Composing transformations
Projection parms.
Parameters:
rotation and
translation
cancel
A
W
T =
A
C
T(p)
C
W
T (w)
Two transformations are composed to get one
transformation that maps points from the world frame
to the frame A
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Deriving form of the camera matrix
We have already described what the
camera matrix does and what form it
has; we now go through the steps to
justify it
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Viewing model points M
What’s in front of the camera?
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Math for the steps
Camera C maps
3D points in
world W to 2D
pixels in image I
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Perspective transformation: camera
origin at the center of projection
This transformation
uses same units in 3D
as in the image plane
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Perspective projection: camera
origin in the real image plane
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Rigid transformation for change
of coordinate frame
3D coordinate
frame of camera
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Relate camera frame to world frame
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Change scene units to pixels
To get into XV or GIMP
image coordinates! This is
a 2D to 2D transformation.
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Final result
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a 3x4 camera matrix
maps 2D image pixels to 3D rays
maps 3D rays to 2D image pixels
obtain matrix via calibration (easy)
obtain matrix via reasoning (hard)
do camera calibration exercise
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The camera model, or matrix, is 3 x 4 and
maps a homogeneous point in the world to a
homogeneous pixel in the image. The ‘1’ is
used to model translation
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