Projective Geometry

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CSE 576, Spring 2008 Projective Geometry 1 Projective Geometry Lecture slides by Steve Seitz (mostly) Lecture presented by Rick Szeliski

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Projective Geometry. Lecture slides by Steve Seitz (mostly) Lecture presented by Rick Szeliski. Final project ideas. Discussion by Steve Seitz and Rick Szeliski …. Projective geometry. Readings - PowerPoint PPT Presentation

Transcript of Projective Geometry

Page 1: Projective Geometry

CSE 576, Spring 2008 Projective Geometry 1

Projective Geometry

Lecture slides by Steve Seitz (mostly)Lecture presented by Rick Szeliski

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CSE 576, Spring 2008 Projective Geometry 2

Final project ideasDiscussion by Steve Seitz and Rick Szeliski

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Projective geometry

Readings• Mundy, J.L. and Zisserman, A., Geometric Invariance in Computer Vision, Appendix:

Projective Geometry for Machine Vision, MIT Press, Cambridge, MA, 1992, (read  23.1 - 23.5, 23.10)

– available online: http://www.cs.cmu.edu/~ph/869/papers/zisser-mundy.pdf

Ames Room

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Projective geometry—what’s it good for?

Uses of projective geometry• Drawing• Measurements• Mathematics for projection• Undistorting images• Focus of expansion• Camera pose estimation, match move• Object recognition

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Applications of projective geometry

Vermeer’s Music Lesson

Reconstructions by Criminisi et al.

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1 2 3 4

1

2

3

4

Measurements on planes

Approach: unwarp then measure

What kind of warp is this?

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Image rectification

To unwarp (rectify) an image• solve for homography H given p and p’• solve equations of the form: wp’ = Hp

– linear in unknowns: w and coefficients of H

– H is defined up to an arbitrary scale factor

– how many points are necessary to solve for H?

pp’

work out on board

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Solving for homographies

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Solving for homographies

A h 0

Defines a least squares problem:

2n × 9 9 2n

• Since h is only defined up to scale, solve for unit vector ĥ• Solution: ĥ = eigenvector of ATA with smallest eigenvalue• Works with 4 or more points

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(0,0,0)

The projective planeWhy do we need homogeneous coordinates?

• represent points at infinity, homographies, perspective projection, multi-view relationships

What is the geometric intuition?• a point in the image is a ray in projective space

(sx,sy,s)

• Each point (x,y) on the plane is represented by a ray (sx,sy,s)– all points on the ray are equivalent: (x, y, 1) (sx, sy, s)

image plane

(x,y,1)-y

x-z

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Projective linesWhat does a line in the image correspond to in

projective space?

• A line is a plane of rays through origin– all rays (x,y,z) satisfying: ax + by + cz = 0

z

y

x

cba0 :notationvectorin

• A line is also represented as a homogeneous 3-vector ll p

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l

Point and line duality• A line l is a homogeneous 3-vector• It is to every point (ray) p on the line: l p=0

p1p2

What is the intersection of two lines l1 and l2 ?

• p is to l1 and l2 p = l1 l2

Points and lines are dual in projective space• given any formula, can switch the meanings of points and

lines to get another formula

l1

l2

p

What is the line l spanned by rays p1 and p2 ?

• l is to p1 and p2 l = p1 p2

• l is the plane normal

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Ideal points and lines

Ideal point (“point at infinity”)• p (x, y, 0) – parallel to image plane• It has infinite image coordinates

(sx,sy,0)-y

x-z image plane

Ideal line• l (a, b, 0) – parallel to image plane

(a,b,0)-y

x-z image plane

• Corresponds to a line in the image (finite coordinates)– goes through image origin (principle point)

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Homographies of points and linesComputed by 3x3 matrix multiplication

• To transform a point: p’ = Hp• To transform a line: lp=0 l’p’=0

– 0 = lp = lH-1Hp = lH-1p’ l’ = lH-1

– lines are transformed by postmultiplication of H-1

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3D projective geometryThese concepts generalize naturally to 3D

• Homogeneous coordinates– Projective 3D points have four coords: P = (X,Y,Z,W)

• Duality– A plane N is also represented by a 4-vector

– Points and planes are dual in 3D: N P=0

• Projective transformations– Represented by 4x4 matrices T: P’ = TP, N’ = N T-1

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3D to 2D: “perspective” projection

Matrix Projection: ΠPp

1************

ZYX

wwywx

What is not preserved under perspective projection?

What IS preserved?

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Vanishing points

image plane

line on ground plane

vanishing point v

Vanishing point• projection of a point at infinity

cameracenter

C

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Vanishing points

Properties• Any two parallel lines have the same vanishing point v• The ray from C through v is parallel to the lines• An image may have more than one vanishing point

– in fact every pixel is a potential vanishing point

image plane

cameracenter

C

line on ground plane

vanishing point v

line on ground plane

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Vanishing lines

Multiple Vanishing Points• Any set of parallel lines on the plane define a vanishing point• The union of all of vanishing points from lines on the same

plane is the vanishing line– For the ground plane, this is called the horizon

v1 v2

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Vanishing lines

Multiple Vanishing Points• Different planes define different vanishing lines

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Computing vanishing points

V

DPP t 0

1ZZ

YY

XX

t tDP

tDP

tDP

P

P0

D

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0/1

/

/

/

1Z

Y

X

ZZ

YY

XX

ZZ

YY

XX

t D

D

D

t

t

DtP

DtP

DtP

tDP

tDP

tDP

PP

Computing vanishing points

Properties• P is a point at infinity, v is its projection

• They depend only on line direction

• Parallel lines P0 + tD, P1 + tD intersect at P

V

DPP t 0

ΠPv

P0

D

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Computing the horizon

Properties• l is intersection of horizontal plane through C with image plane

• Compute l from two sets of parallel lines on ground plane

• All points at same height as C project to l– points higher than C project above l

• Provides way of comparing height of objects in the scene

ground plane

lC

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Fun with vanishing points

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Perspective cues

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Perspective cues

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Perspective cues

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Comparing heights

VanishingVanishingPointPoint

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Measuring height

1

2

3

4

55.4

2.8

3.3

Camera height

What is the height of the camera?

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q1

Computing vanishing points (from lines)

Intersect p1q1 with p2q2

v

p1

p2

q2

Least squares version• Better to use more than two lines and compute the “closest” point of

intersection

• See notes by Bob Collins for one good way of doing this:– http://www-2.cs.cmu.edu/~ph/869/www/notes/vanishing.txt

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C

Measuring height without a ruler

ground plane

Compute Z from image measurements• Need more than vanishing points to do this

Z

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The cross ratio

A Projective Invariant• Something that does not change under projective transformations

(including perspective projection)

P1

P2

P3

P4

1423

2413

PPPP

PPPP

The cross-ratio of 4 collinear points

Can permute the point ordering• 4! = 24 different orders (but only 6 distinct values)

This is the fundamental invariant of projective geometry

1i

i

i

i Z

Y

X

P

3421

2431

PPPP

PPPP

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vZ

r

t

b

tvbr

rvbt

Z

Z

image cross ratio

Measuring height

B (bottom of object)

T (top of object)

R (reference point)

ground plane

HC

TBR

RBT

scene cross ratio

1

Z

Y

X

P

1

y

x

pscene points represented as image points as

R

H

R

H

R

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Measuring height

RH

vz

r

b

t

R

H

Z

Z

tvbr

rvbt

image cross ratio

H

b0

t0

vvx vy

vanishing line (horizon)

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Measuring height vz

r

b

t0

vx vy

vanishing line (horizon)

v

t0

m0

What if the point on the ground plane b0 is not known?

• Here the guy is standing on the box, height of box is known

• Use one side of the box to help find b0 as shown above

b0

t1

b1

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Computing (X,Y,Z) coordinates

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3D Modeling from a photograph

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Camera calibrationGoal: estimate the camera parameters

• Version 1: solve for projection matrix

ΠXx

1************

ZYX

wwywx

• Version 2: solve for camera parameters separately– intrinsics (focal length, principle point, pixel size)

– extrinsics (rotation angles, translation)

– radial distortion

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Vanishing points and projection matrix

************

Π 4321 ππππ

1π 2π 3π 4π

T00011 Ππ = vx (X vanishing point)

Z3Y2 , similarly, vπvπ

origin worldof projection10004 TΠπ

ovvvΠ ZYXNot So Fast! We only know v’s and o up to a scale factor

ovvvΠ dcba ZYX• Need a bit more work to get these scale factors…

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Finding the scale factors…Let’s assume that the camera is reasonable

• Square pixels• Image plane parallel to sensor plane• Principal point in the center of the image

1000

100

010

001

1000

0

0

0

0100

0010

0001

/100

010

001

3

2

1

333231

232221

131211

t

t

t

rrr

rrr

rrr

f

Π

ftfrfrfr

trrr

trrr

/'///

'

'

3333231

2232221

1131211

ovvv dcba ZYX

3

2

1

333231

232221

131211

3

2

1

'

'

'

t

t

t

rrr

rrr

rrr

t

t

t

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Solving for f

dtcrbrar

dtcrbrar

dtcrbrar

fofvfvfv

ovvv

ovvv

zyx

zyx

zyx

/'///

/'///

/'///

3333231

2232221

1131211

3333

2222

1111

Orthogonal vectors

Orthogonal vectors

dftcfrbfrafr

dtcrbrar

dtcrbrar

ovvv

ovvv

ovvv

zyx

zyx

zyx

/'///

/'///

/'///

3333231

2232221

1131211

3333

2222

1111

ovvv ZYX

0332

2211 yxyxyx vvfvvvv33

2211

yx

yxyx

vv

vvvvf

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Solving for a, b, and c

dtcrbrar

dtcrbrar

dtcrbrar

ovvv

fofvfvfv

fofvfvfv

zyx

zyx

zyx

/'///

/'///

/'///

////

////

3333231

2232221

1131211

3333

2222

1111

Norm = 1/a

Norm = 1/a

Solve for a, b, c• Divide the first two rows by f, now that it is known• Now just find the norms of the first three columns• Once we know a, b, and c, that also determines R

How about d?• Need a reference point in the scene

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Solving for d

Suppose we have one reference height H• E.g., we known that (0, 0, H) gets mapped to (u, v)

1

0

0

/'

/'

/'

3333231

2232221

1131211

Hdtrrr

dtrrr

dtrrr

w

wv

wu

dtHr

dtHru

/'

/'

333

113

HrHur

uttd

1333

31 ''

Finally, we can solve for t

3

2

1

333231

232221

131211

3

2

1

'

'

'T

t

t

t

rrr

rrr

rrr

t

t

t

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Calibration using a reference object

Place a known object in the scene• identify correspondence between image and scene• compute mapping from scene to image

Issues• must know geometry very accurately• must know 3D->2D correspondence

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Chromaglyphs

Courtesy of Bruce Culbertson, HP Labshttp://www.hpl.hp.com/personal/Bruce_Culbertson/ibr98/chromagl.htm

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Estimating the projection matrix

Place a known object in the scene• identify correspondence between image and scene• compute mapping from scene to image

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Direct linear calibration

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Direct linear calibration

Can solve for mij by linear least squares• use eigenvector trick that we used for homographies

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Direct linear calibration

Advantage:• Very simple to formulate and solve

Disadvantages:• Doesn’t tell you the camera parameters• Doesn’t model radial distortion• Hard to impose constraints (e.g., known focal length)• Doesn’t minimize the right error function

For these reasons, nonlinear methods are preferred• Define error function E between projected 3D points and image positions

– E is nonlinear function of intrinsics, extrinsics, radial distortion

• Minimize E using nonlinear optimization techniques

– e.g., variants of Newton’s method (e.g., Levenberg Marquart)

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Alternative: multi-plane calibration

Images courtesy Jean-Yves Bouguet, Intel Corp.

Advantage• Only requires a plane• Don’t have to know positions/orientations• Good code available online!

– Intel’s OpenCV library: http://www.intel.com/research/mrl/research/opencv/

– Matlab version by Jean-Yves Bouget: http://www.vision.caltech.edu/bouguetj/calib_doc/index.html

– Zhengyou Zhang’s web site: http://research.microsoft.com/~zhang/Calib/

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Some Related TechniquesImage-Based Modeling and Photo Editing

• Mok et al., SIGGRAPH 2001• http://graphics.csail.mit.edu/ibedit/

Single View Modeling of Free-Form Scenes• Zhang et al., CVPR 2001• http://grail.cs.washington.edu/projects/svm/

Tour Into The Picture• Anjyo et al., SIGGRAPH 1997• http://koigakubo.hitachi.co.jp/little/DL_TipE.html