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CS 39549525: Spring 2003

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Cameras as Protractors. P = (K-TK-1) = = Image of Absolute Conic' ... Given ONLY images from 2 cameras C, C' Different views of same objects X, but ... – PowerPoint PPT presentation

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Title: CS 39549525: Spring 2003


1
CS 395/495-25 Spring 2003
  • IBMR Week 8A
  • Chapter 8... Epipolar Geometry Two Cameras
  • Jack Tumblin
  • jet_at_cs.northwestern.edu

2
IBMR-Related Seminars
  • Light Scattering ModelsFor Rendering Human Hair
  • Steve Marschner, Cornell University Friday May
    23 300pm, Rm 381, CS Dept.

3
Reminders
  • ProjA graded Good Job! 90,95, 110
  • ProjB graded Good! minor H confusions.
  • MidTerm graded novel solutions encouraged.
  • ProjC due Friday, May 16 many recd...
  • ProjD posted, due Friday May 30
  • (Last Weeks IAC notes revised...)
  • Take-Home Final Exam Thurs June 5, due June 11

4
Camera Matrix P links P3?P2
  • Basic camera
  • x P0 X where P0 K 0
  • World-space camera
  • translate X to camera location C, then
    rotate x PX (P0RT) X
  • Rewrite as
  • P KR -RC
  • Redundant notationP M p4M KRp4 -K
    R C




5
Chapter 7 More One-Camera Details
  • Full 3x4 camera matrix P maps P3world to P2 image
  • ? What does it do to basic 3D world shapes?
  • Forward Projection
  • Line / Ray in world ? Line/Ray in image
  • Ray in P3 is X(?) A ?B
  • Camera changes to P2 x(?) PA ?PB

yc
A
PA
?B
p
C
f
zc
xc
6
Chapter 7 More One-Camera Details
  • Full 3x4 camera matrix P maps P3world to P2 image
  • ? What does it do to basic 3D world shapes?
  • Backward Projection
  • Line L in image ? Plane ?L in world
  • Recall Line L in P2 (a 3-vector) L x1 x2
    x3T
  • Plane ?L in P3 (a 4-vector)
  • ?L PTL

p11 p21 p31 p12 p22 p32p13 p23 p33p14
p24 p34
x1x2x3
yc
L
zc
p
C
f
xc
7
Cameras as Protractors
  • World-Space Direction D xc, yc, zc, 0T
  • Direction from image point x D (KR)-1x
  • Point C and points x1,x2 form angle ? (pg
    199)
  • (K-TK-1) ? Image of Absolute Conic
  • Line L makes plane ? with normal n KT L (in
    camera coords)

8
Cameras as Protractors
  • P (??) (K-TK-1). OK. Now what was ?? again?
  • Absolute Conic ?? all imaginary points on ??
  • Satisfies BOTH x12 x22 x32 0 AND x42 0
  • Finds right angles-- if D1 ? D2, then D1T
    ??D2 0
  • Dual of absolute conic is Dual Quadric Q?
    all imaginary planes ? ? to ??, tangent to Q?
  • Finds right angles-- if ?1 ? ?2, then ?1T
    Q??2 0
  • can write ?? or Q? as the same matrix

1 0 0 00 1 0 00 0 1 00 0 0 0
9
Cameras as Protractors
  • Clever vanishing point trick
  • Perpendicular lines in image?
  • Find their vanishing pts. by construction
  • Use v1T? v2 0, stack, solve for ? (K-TK-1)

v3
v2
v1
10
Cameras as Protractors
  • P ?? (K-TK-1) ? Image of Absolute Conic
  • Just ??as has a dual Q?, ? has dual ?
  • ? ?-1 K KT
  • The dual conic ? is the image of Q? , so
  • ? P (Q?) P( )
  • Vanishing points v1,v2 of 2 ? world-space
    lines v1T? v2 0
  • Vanishing lines L1, L2 of 2 ? world-space
    planes L1T? L2 0

11
Movement Detection?
  • Can we do it from images only?
  • 2D projective transforms often LOOK like 3-D
  • External cam. calib. affects all elements of P
  • YES. Camera moved if--only-ifCamera-ray points
    (C?x?X1,X2, etc) will
  • map to LINE (not a point) in the other image
  • Epipolar Line l image of L
  • Parallax x1?x2 vector

X2
X1
x2
x1
L
x
C
l
C
12
Epipolar Geometry Chapter 8
  • Basic idea
  • Given ONLY images from 2 cameras C, C
  • Different views of same objects X, butwe dont
    know world-space points X.
  • If we choose an x, how can we find x ?
  • How are x, x linked?

X
x
x
C
C
13
Epipolar Geometry Chapter 8
  • Basic idea
  • 2 cameras located at C, C in world space.
  • Find baseline through camera centers C, C
  • Baseline hits image planes at epipoles
  • Notice baseline and Xform a plane...
  • Many OTHER planes thru baseline ...

X
x
x
baseline
C
C
14
Epipolar Geometry Chapter 8
  • Basic idea
  • 2 cameras located at C, C in world space.
  • Find baseline through camera centers C, C
  • Baseline hits image planes at epipoles
  • Family of planes thru baseline are all the
    epipolar planes
  • Image of planes lines epipolar lines
  • Lines intersect at epipolar points in both
    images.

x
x
baseline
C
C
15
Epipolar Geometry Chapter 8
X
epipolar plane ?
epipolar line L
epipolar line L
x
x
baseline
C
e
e
C
  • Summary
  • Connect cameras C, C with a baseline, which
  • hits image planes at epipoles e, e.
  • Chose any world pt X, then ?? everything is
    coplanar! epipolar plane ? includes image points
    x, x, and these connect to epipoles e,e by
    epipolar lines L, L

16
Epipolar Geometry
X
epipolar plane ?
epipolar line L
epipolar line L
x
x
baseline
C
e
e
C
  • Useful properties
  • Every image point x maps to an epipolar line
    Lalso
  • Epipoles e,e each cameras view of the other
  • All epipolar lines L pass through epipole e
  • Epipolar Line L is (image of the C?X ray...)
  • Epipolar Line L links (image of C) to (image of
    X)

17
Fundamental Matrix Fx L
X
epipolar plane ?
epipolar line L
epipolar line L
x
x
baseline
C
e
e
C
  • One Matrix Summarizes ALL of Epipolar Geometry
  • Fundamental Matrix F 3x3, rank 2.
  • Maps image point x to image point x xT F x
    0
  • but F is only Rank 2 given only x, F cannot
    find x for you!!
  • Maps image point x to epipolar line L F x
    L

18
Fundamental Matrix Fx L
X
epipolar plane ?
epipolar line L
epipolar line L
X(?)
x
x
baseline
C
e
C
  • 1) How do we find F?
  • If we know the camera matrices P and P
  • (we almost never do), book derives (pg 224)
  • F e? P P ?!?!What?!?! point e cross
    product with a matrix pp ?!?!

(Recall P PT(PPT)-1, the pseudo-inverse)
19
Fundamental Matrix Fx L
X
epipolar plane ?
epipolar line L
epipolar line L
X(?)
x
x
baseline
C
e
C
  • F e? P P But whats this? NEW TRICK
  • Cross Product written as matrix multiply (pg.
    554)
  • a ? b ?
    a?b
  • Note a ? b -b ? a a?b (aTb?)T

a1 a2 a3
b1 b2 b3
a2b3 a3b2 a3b1 a1b3 a1b2 a2b1
0 -a3 a2 a3 0 -a1-a2 a1 0
b1 b2 b3
skew symmetric matrix
20
Fundamental Matrix Fx L
X
epipolar plane ?
epipolar line L
epipolar line L
X(?)
x
x
baseline
C
e
C
  • 2) What is F if we DONT know the cameras P,
    P,but we DO know some corresp. point pairs (x,
    x)?
  • F finds epipolar line L from point x Fx L
  • (Recall that if (any) point x is on line a L,
    then xT L 0)
  • Substitute Fx for L xTF x 0
  • AHA! we can find F using DLT-like method! see
    Chap. 10

21
Fundamental Matrix Summary
(pg. 226)
  • F is 3x3 matrix, maps P2?P2, rank 2, 7-DOF
  • If world space pt X ? image space pts. x and x
    then xTF x 0
  • Every image pt has epipolar line in the other
    image Fx L FTx L
  • Baseline pierces image planes at epipoles e, e
  • Fe 0 FTe 0

22
Fundamental Matrix Summary
(pg. 226)
  • F is 3x3 matrix, maps P2?P2, rank 2, 7-DOF
  • Given camera matrices P, P, find F matrix by
  • F e? P P (recall e is image of C e
    PC)
  • F is unaffected by any proj. transforms done on
    BOTH cameras(PH, PH) has same F matrix as (P,
    P) for any full-rank H
  • (e.g. F measures camera C vs. Camera C only,
    no matter where you put them)

23
Fundamental Matrix Uses
  • Special case camera translate only (no
    rotations)
  • Camera matrices are P K I 0 , P KI t
  • where K is internal calib., t is 3D translation
    vector
  • F matrix simplifies to F e?
  • Epipolar lines are all parallel to direction t
  • x,x displacement depends only on t 3D depth z
  • x x (Kt)(1/zc)

24
Fundamental Matrix Uses
  • Can we find a camera matrix from motion
    fundamental F?
  • Let one camera position define the worlds
    coordsP P0 K I 0 , and other is P
    M m KR -RC
  • where K is internal calib., R is rotation C is
    position
  • F matrix simplifies to F m?M
  • If we know how we moved the camera (R,C matrices)
    then find F by correspondence and solve for K.
    (pg 237)
  • No R, C matrices? Use Essential Matrix (pg 238)



25
Fundamental Matrix Properties
(pg. 226)
  • Why bother with F?
  • Can find it from image pt. correspondences only
  • Works even for mismatched cameras (example
    100-year time-lapse of Eiffel tower)
  • Choose your own world-space coordinate system.
  • SVD lets us recover P0, P camera matrices from F
  • (See 8.6 The Essential matrixpg 240)
  • Complete 2-camera mapping from world??image
  • 2 images corresponding point pairs (xi,xi)?F
  • Let camera coords 3D world coords, then
    (xi,xi)?Xi

26
Correspondence Problem
  • Where Computer Vision, IBMR part ways
  • Fundamental Matrix Corresponding point pairs
    (x,x) ?
  • How can we blunt the correspondence problem?

27
END
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