Transcript Zhang`s Camera Calibration

Image Rectification
for Stereo Vision
Charles Loop
Zhengyou Zhang
Microsoft Research
Problem Statement
 Compute
a pair of 2D projective transforms
(homographies)
Original images
Rectified images
Motivations
 To
simplify stereo matching:
Instead of comparing pixels on skew lines, we now only
compare pixels on the same scan lines.
 Graphics
applications: view morphing
 Problem:
Rectifying homographies are not unique
 Goal: to develop a technique based on
geometrically well-defined criteria minimizing
image distortion due to rectification
Epipolar Geometry
M
m
m’
C
C’
•Epipoles anywhere
•Fundamental matrix
F: a 3x3 rank-2 matrix

•Epipole at i  1 0
•Fundamental matrix
0 0 0 
F  [i ]  0 0  1
0 1 0 
0
T
Stereo Image Rectification
H and H’ such that
 Compute rectified image points:
 Compute
 Problem:
H and H’ are not unique.
Properties of H and H’ (I)
 Consider
 Recall:




each row of H and H’ as a line:
both e and e’ are sent to [1 0 0]T
Observations (I):
v and w must go through the epipole e
v’ and w’ must go through the epipole e’
u and u’ are irrelevant to rectification
Properties of H and H’ (II)

Observation (II):
Lines v and v’, and lines w and w’ must be corresponding
epipolar lines.

Observation (III):
Lines w and w’ define the rectifying plane.
Decomposition of H
H  HsHr H p

Special projective transform:

Similarity transform:

Shearing transform:
Special Projective Transform
(I)
 Sends
the epipole to infinity

epipolar lines become parallel
 Captures all image distortion due to
projective transformation
 Subgoal: Make Hp as affine as possible.
Special Projective Transform
(II)
How to do it?
 Let original image point be
 the transformed point will be
with weight
 Observation:
If all weights are equal, then there is no distortion.
 Key
idea:
minimize the variation of wi over all pixels
Similarity Transform
 Rotate
and translate images such that the
epipolar lines are horizontally aligned.
 Images
are now rectified.
Shearing Transform
 Free
to scale and translate in the horizontal
direction.
 Subgoal:
Preserve original image resolution as close
as possible.
Example
 Original
image pair
Intermediate result
 After
special projective transform:
Intermediate result
 After
similarity transform:
Final result
 After
shearing transform