Lecture'9'&'10:'' Stereo'Vision'vision.stanford.edu/.../lecture9_10_stereo_cs131_2016.pdfFei-Fei Li...
Transcript of Lecture'9'&'10:'' Stereo'Vision'vision.stanford.edu/.../lecture9_10_stereo_cs131_2016.pdfFei-Fei Li...
Lecture 9 & 10 - Fei-Fei Li
Lecture'9'&'10:''Stereo'Vision'
Dr.'Juan'Carlos'Niebles'Stanford'AI'Lab'
'Professor'FeiAFei'Li'Stanford'Vision'Lab'
25AOctA16'1'
Lecture 9 & 10 - Fei-Fei Li
Point of observation
Figures © Stephen E. Palmer, 2002
Dimensionality'ReducIon'Machine'(3D'to'2D)'
3D world 2D image
25AOctA16'2'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'3'
Pinhole(camera(
• 'Common'to'draw'image'plane'in#front''of'the'focal'point'''• 'Moving'the'image'plane'merely'scales'the'image.'
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Lecture 9 & 10 - Fei-Fei Li 25AOctA16'4'
RelaIng'a'realAworld'point'to'a'point'on'the'camera'
In'homogeneous'coordinates:'
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Intrinsic Assumptions • Unit aspect ratio • Optical center at (0,0) • No skew
Extrinsic Assumptions • No rotation • Camera at (0,0,0)
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'5'
RelaIng'a'realAworld'point'to'a'point'on'the'camera'
In'homogeneous'coordinates:'
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Intrinsic Assumptions • Unit aspect ratio • Optical center at (0,0) • No skew
Extrinsic Assumptions • No rotation • Camera at (0,0,0)
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Lecture 9 & 10 - Fei-Fei Li
RealAworld'camera'
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Extrinsic Assumptions • No rotation • Camera at (0,0,0)
Intrinsic Assumptions • Optical center at (u0, v0) • Rectangular pixels • Small skew
Intrinsic'parameters'
P ' = K I 0!"
#$P
Slide'inspiraIon:'S.'Savarese'
25AOctA16'6'
Lecture 9 & 10 - Fei-Fei Li
RealAworld'camera'+'RealAworld'transformaIon'
Extrinsic Assumptions • Allow rotation • Camera at (tx,ty,tz)
Intrinsic Assumptions • Optical center at (u0, v0) • Rectangular pixels • Small skew
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Slide'inspiraIon:'S.'Savarese'
25AOctA16'7'
Lecture 9 & 10 - Fei-Fei Li
What'we'will'learn'today?'
• IntroducIon'to'stereo'vision'• Epipolar'geometry:'a'gentle'intro'
• Parallel'images'&'image'recIficaIon'
• Solving'the'correspondence'problem'
• Homographic'transformaIon'
• AcIve'stereo'vision'system'
'
25AOctA16'8'
Reading:('[HZ]'Chapters:'4,'9,'11''[FP]'Chapters:'10 ''
Lecture 9 & 10 - Fei-Fei Li
What'we'will'learn'today?'
• IntroducIon'to'stereo'vision'• Epipolar'geometry:'a'gentle'intro'
• Parallel'images'&'image'recIficaIon'
• Solving'the'correspondence'problem'
• Homographic'transformaIon'
• AcIve'stereo'vision'system'
'
25AOctA16'9'
Reading:('[HZ]'Chapters:'4,'9,'11''[FP]'Chapters:'10 ''
Lecture 9 & 10 - Fei-Fei Li
Recovering'3D'from'Images'
• How'can'we'automaIcally'compute'3D'geometry'from'images?'– What'cues'in'the'image'provide'3D'informaIon?'
Point of observation
Real 3D world 2D image
?'
25AOctA16'10'
Lecture 9 & 10 - Fei-Fei Li
• Shading'
Visual'Cues'for'3D'
Merle(Norman(Cosme5cs,(Los(Angeles(
Slide'cred
it:'J.'H
ayes'
25AOctA16'11'
Lecture 9 & 10 - Fei-Fei Li
Visual'Cues'for'3D'
• Shading'
• Texture'
The$Visual$Cliff,(by(William(Vandivert,(1960(
Slide'cred
it:'J.'H
ayes'
25AOctA16'12'
Lecture 9 & 10 - Fei-Fei Li
Visual'Cues'for'3D'
From(The$Art$of$Photography,(Canon(
• Shading'
• Texture'
• Focus'
Slide'cred
it:'J.'H
ayes'
25AOctA16'13'
Lecture 9 & 10 - Fei-Fei Li
Visual'Cues'for'3D'
• Shading'
• Texture'
• Focus'
• MoIon'
Slide'cred
it:'J.'H
ayes'
25AOctA16'14'
Lecture 9 & 10 - Fei-Fei Li
Visual'Cues'for'3D'
• Others:'– Highlights'– Shadows'– Silhouedes'– InterAreflecIons'– Symmetry'
– Light'PolarizaIon'– ...'
• Shading'
• Texture'
• Focus'
• MoIon'Shape'From'X'
• X'='shading,'texture,'focus,'moIon,'...'
• We’ll'focus'on'the'moIon'cue'
Slide'cred
it:'J.'H
ayes'
25AOctA16'15'
Lecture 9 & 10 - Fei-Fei Li
Stereo'ReconstrucIon'
• The'Stereo'Problem'
– Shape'from'two'(or'more)'images'
– Biological'moIvaIon'
known(camera(
viewpoints(
Slide'cred
it:'J.'H
ayes'
25AOctA16'16'
Lecture 9 & 10 - Fei-Fei Li
Why'do'we'have'two'eyes?'
Cyclops( ( (((((((((((( (vs.(((((((((((((((((((((( ((Odysseus(
Slide'cred
it:'J.'H
ayes'
25AOctA16'17'
Lecture 9 & 10 - Fei-Fei Li
1.'Two'is'beder'than'one'
Slide'cred
it:'J.'H
ayes'
25AOctA16'18'
Lecture 9 & 10 - Fei-Fei Li
Depth'from'Convergence'
Slide'cred
it:'J.'H
ayes'
25AOctA16'19'
Lecture 9 & 10 - Fei-Fei Li
What'we'will'learn'today?'
• IntroducIon'to'stereo'vision'• Epipolar'geometry:'a'gentle'intro'
• Parallel'images'&'image'recIficaIon'
• Solving'the'correspondence'problem'
• Homographic'transformaIon'
• AcIve'stereo'vision'system'
'
25AOctA16'20'
Reading:('[HZ]'Chapters:'4,'9,'11''[FP]'Chapters:'10 ''
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'21'
Pinhole(camera(
• 'Common'to'draw'image'plane'in#front''of'the'focal'point'''• 'Moving'the'image'plane'merely'scales'the'image.'
f' f'
!!"
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Lecture 9 & 10 - Fei-Fei Li 25AOctA16'22'
Triangula5on(
O' O’'
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P'
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Inputs:'1. Camera'parameters'known'2. Corresponding'points'p'and'p’'known''
Output:'P'is'at'the'intersecIon'of'''''''''''and'
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Lecture 9 & 10 - Fei-Fei Li 25AOctA16'23'
Epipolar(geometry(
• 'Epipolar'Plane' • 'Epipoles'e,'e’'
• 'Epipolar'Lines'• 'Baseline'
O' O’'
p’'
P'
p'
e' e’'
='intersecIons'of'baseline'with'image'planes''='projecIons'of'the'other'camera'center'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'24'
Example:(Converging(image(planes(
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'25'
Epipolar(Constraint(
A 'Two'views'of'the'same'object''A 'Suppose'I'know'the'camera'posiIons'and'camera'matrices'A'Given'a'point'on'len'image,'how'can'I'find'the'corresponding'point'on'right'image?'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'26'
O' O’'
P?'
p'
e' e’'l’'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'27'
Epipolar(Constraint(
• ''PotenIal'matches'for'p'have'to'lie'on'the'corresponding'epipolar'line'l’.'
• ''PotenIal'matches'for'p’'have'to'lie'on'the'corresponding'epipolar'line'l.'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'28'
Epipolar(Constraint(
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Lecture 9 & 10 - Fei-Fei Li 25AOctA16'29'
Epipolar(Constraint(
O' O’'
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'''K'and'K’'are'known''''(calibrated'cameras)'
M = K I 0!"
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[ ]0IM = [ ]TRM ='Assume'K'='K’'='I'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'30'
Epipolar(Constraint(
O' O’'
p'p’'
P'
R,'T'
[ ] 0)pR(TpT =!×⋅Perpendicular'to'epipolar'plane'
)( pRT !×
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'31'
Cross'product'as'matrix'mulIplicaIon'
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Lecture 9 & 10 - Fei-Fei Li 25AOctA16'32'
Epipolar(Constraint(
O' O’'
p'p’'
P'
R,'T'
[ ] 0)pR(TpT =!×⋅ [ ] 0pRTpT =!⋅⋅→ ×
E'='essenIal'matrix'(LonguetAHiggins,'1981)'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'33'
Epipolar(Constraint(
• E'p’''is'the'epipolar'line'associated'with'p’'(l'='E'p’)'• ET'p''is'the'epipolar'line'associated'with'p'(l’'='ET'p)'• E'is'singular'(rank'two)'• E'e’'='0'''and'''ET'e'='0'• E'is'3x3'matrix;'5'DOF''
O'O’'
p’'
P'
p'
e' e’'
l' l’'
Lecture 9 & 10 - Fei-Fei Li
What'we'will'learn'today?'
• IntroducIon'to'stereo'vision'• Epipolar'geometry:'a'gentle'intro'
• Parallel'images'&'image'recIficaIon'
• Solving'the'correspondence'problem'
• Homographic'transformaIon'
• AcIve'stereo'vision'system'
'
25AOctA16'34'
Reading:('[HZ]'Chapters:'4,'9,'11''[FP]'Chapters:'10 ''
Lecture 9 & 10 - Fei-Fei Li
Simplest Case: Parallel images • Image planes of cameras
are parallel to each other and to the baseline
• Camera centers are at same height
• Focal lengths are the same
Slide'cred
it:'J.'H
ayes'
25AOctA16'35'
Lecture 9 & 10 - Fei-Fei Li
• Image planes of cameras are parallel to each other and to the baseline
• Camera centers are at same height
• Focal lengths are the same • Then, epipolar lines fall
along the horizontal scan lines of the images
Simplest Case: Parallel images
Slide'cred
it:'J.'H
ayes'
25AOctA16'36'
Lecture 9 & 10 - Fei-Fei Li
Essential matrix for parallel images
pTE !p = 0, E = [t× ]R
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Epipolar constraint:
t
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Reminder:'skew'symmetric'matrix'
Slide'cred
it:'J.'H
ayes'
25AOctA16'37'
Lecture 9 & 10 - Fei-Fei Li
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Epipolar constraint:
Slide'cred
it:'J.'H
ayes'
25AOctA16'38'
Lecture 9 & 10 - Fei-Fei Li
Triangulation -- depth from disparity
f
p p�
Baseline B
z
O O�
P
f
disparity = u− "u = B ⋅ fz
Disparity is inversely proportional to depth!
Slide'cred
it:'J.'H
ayes'
25AOctA16'39'
Lecture 9 & 10 - Fei-Fei Li
Stereo image rectification
Slide'cred
it:'J.'H
ayes'
25AOctA16'40'
Lecture 9 & 10 - Fei-Fei Li
Algorithm: • Re-project image planes onto a
common plane parallel to the line between optical centers
• Pixel motion is horizontal after this transformation
• Two transformation matrices, one for each input image reprojection
! C. Loop and Z. Zhang. Computing Rectifying Homographies for Stereo Vision. IEEE Conf. Computer Vision and Pattern Recognition, 1999.
Stereo image rectification
Slide'cred
it:'J.'H
ayes'
25AOctA16'41'
Lecture 9 & 10 - Fei-Fei Li
Rectification example
25AOctA16'42'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'43'
Applica5on:(view(morphing(S.'M.'Seitz'and'C.'R.'Dyer,'Proc.#SIGGRAPH#96,'1996,'21A30''
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'44'
Applica5on:(view(morphing(
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'45'
Applica5on:(view(morphing(
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'46'
Applica5on:(view(morphing(
Lecture 9 & 10 - Fei-Fei Li
Removing perspective distortion
Hp'
(rectification)
25AOctA16'47'
Lecture 9 & 10 - Fei-Fei Li
What'we'will'learn'today?'
• IntroducIon'to'stereo'vision'• Epipolar'geometry:'a'gentle'intro'
• Parallel'images'&'image'recIficaIon'
• Solving'the'correspondence'problem'
• Homographic'transformaIon'
• AcIve'stereo'vision'system'
'
25AOctA16'48'
Reading:('[HZ]'Chapters:'4,'9,'11''[FP]'Chapters:'10 ''
Lecture 9 & 10 - Fei-Fei Li
Stereo matching: solving the correspondence problem
• Goal: finding matching points between two images
25AOctA16'49'
Lecture 9 & 10 - Fei-Fei Li
Basic stereo matching algorithm
• For each pixel in the first image – Find corresponding epipolar line in the right image – Examine all pixels on the epipolar line and pick the best match – Triangulate the matches to get depth information
• Simplest case: epipolar lines are scanlines
– When does this happen?
Slide'cred
it:'J.'H
ayes'
25AOctA16'50'
Lecture 9 & 10 - Fei-Fei Li
Basic stereo matching algorithm
• If necessary, rectify the two stereo images to transform epipolar lines into scanlines
• For each pixel x in the first image – Find corresponding epipolar scanline in the right image – Examine all pixels on the scanline and pick the best match x� – Compute disparity x-x� and set depth(x) = 1/(x-x�)
corresponding'
25AOctA16'51'
Lecture 9 & 10 - Fei-Fei Li
• Let�s make some assumptions to simplify the matching problem – The baseline is relatively small (compared to the
depth of scene points) – Then most scene points are visible in both views – Also, matching regions are similar in appearance
Correspondence problem
Slide'cred
it:'J.'H
ayes'
25AOctA16'52'
Lecture 9 & 10 - Fei-Fei Li
Matching cost
disparity
Left Right
scanline
Correspondence search with similarity constraint
• Slide a window along the right scanline and compare contents of that window with the reference window in the left image
• Matching cost: SSD or normalized correlation
Slide'cred
it:'J.'H
ayes'
25AOctA16'53'
Lecture 9 & 10 - Fei-Fei Li
Left Right
scanline
Correspondence search with similarity constraint
SS
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um o
f squ
ared
diff
eren
ces)
25AOctA16'54'
Lecture 9 & 10 - Fei-Fei Li
Left Right
scanline
Correspondence search with similarity constraint
Nor
mal
ized
cor
rela
tion
25AOctA16'55'
Lecture 9 & 10 - Fei-Fei Li
Effect of window size
– Smaller window + More detail • More noise
– Larger window
+ Smoother disparity maps • Less detail
W = 3 W = 20
25AOctA16'56'
Lecture 9 & 10 - Fei-Fei Li
The similarity constraint
• Corresponding regions in two images should be similar in appearance
• …and non-corresponding regions should be different • When will the similarity constraint fail?
Slide'cred
it:'J.'H
ayes'
25AOctA16'57'
Lecture 9 & 10 - Fei-Fei Li
Limitations of similarity constraint
Textureless surfaces Occlusions, repetition
Specular surfaces Slide'cred
it:'J.'H
ayes'
25AOctA16'58'
Lecture 9 & 10 - Fei-Fei Li
Results with window search
Window-based matching Ground truth
Left image Right image
25AOctA16'59'
Lecture 9 & 10 - Fei-Fei Li
Better methods exist... (CS231a)
Graph cuts Ground truth
Y. Boykov, O. Veksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001
25AOctA16'60'
Lecture 9 & 10 - Fei-Fei Li
The role of the baseline
• Small baseline: large depth error • Large baseline: difficult search problem
Large Baseline Small Baseline
Slide credit: S. Seitz
25AOctA16'61'
Lecture 9 & 10 - Fei-Fei Li
Problem for wide baselines: Foreshortening
• Matching with fixed-size windows will fail! • Possible solution: adaptively vary window size • Another solution: model-based stereo (CS231a)
Slide credit: J. Hayes
25AOctA16'62'
Lecture 9 & 10 - Fei-Fei Li
What'we'will'learn'today?'
• IntroducIon'to'stereo'vision'• Epipolar'geometry:'a'gentle'intro'
• Parallel'images'
• Solving'the'correspondence'problem'
• Homographic'transformaIon'
• AcIve'stereo'vision'system'
'
25AOctA16'63'
Reading:('[HZ]'Chapters:'4,'9,'11''[FP]'Chapters:'10 ''
Lecture 9 & 10 - Fei-Fei Li
Reminder:'transformaIons'in'2D'
Special'case'from'lecture'2'(planar'rotaIon''&'translaIon)'
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- 3 DOF - Preserve distance (areas) - Regulate motion of rigid object
25AOctA16'64'
Lecture 9 & 10 - Fei-Fei Li
u 'v '1
!
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a1 a2 a3a4 a5 a6a7 a8 a9
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- 8 DOF - Preserve colinearity
Reminder:'transformaIons'in'2D'
Generic'case'(rotaIon'in'3D,'scale''&'translaIon)'
25AOctA16'65'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'66'
Goal:'esImate'the'homographic'transformaIon'between'two'images'
H'
AssumpIon:'Given'a'set'of'corresponding'points.'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'67'
Goal:'esImate'the'homographic'transformaIon'between'two'images'
H'
AssumpIon:'Given'a'set'of'corresponding'points.'
QuesIon:'How'many'points'are'needed?'
Hint:'DoF'for'H?' 8!'
At'least'4'points'(8'equaIons)'
Lecture 9 & 10 - Fei-Fei Li
DLT algorithm (Direct Linear Transformation)
pi !pi
!pi = H pi
25AOctA16'68'
H
Lecture 9 & 10 - Fei-Fei Li
DLT algorithm (direct Linear Transformation)
!pi ×H pi = 0
9x1
0Ai =hFunction of measurements
Unknown [9x1]
[2x9]
2'independent'equaIons''
h =
h1h2h9
!
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25AOctA16'69'
H =
h1 h2 h3h4 h5 h6h7 h8 h9
!
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Lecture 9 & 10 - Fei-Fei Li
DLT algorithm (direct Linear Transformation)
0hAi =9x2A
0hA1 =0hA2 =
0hAN =
0hA 199N2 =××
1x9h
Over determined Homogenous system
25AOctA16'70'
pi !pi
H
Lecture 9 & 10 - Fei-Fei Li
DLT algorithm (direct Linear Transformation)
0hA 199N2 =××How to solve ?
Singular Value Decomposition (SVD)!
25AOctA16'71'
Lecture 9 & 10 - Fei-Fei Li
DLT algorithm (direct Linear Transformation)
0hA 199N2 =××
999992 ××× Σ Tn VU
Last column of V gives h! H!
How to solve ?
Singular Value Decomposition (SVD)!
Why?'See'pag'593'of'AZ'
25AOctA16'72'
Lecture 9 & 10 - Fei-Fei Li
DLT algorithm (direct Linear Transformation)
How to solve ? 0hA 199N2 =××
25AOctA16'73'
Lecture 9 & 10 - Fei-Fei Li
Clarification about SVD Tnnnnnmnm VDUP ×××× =
Tnnnmmmnm VDUP ×××× =
Has n orthogonal columns
Orthogonal matrix
• This is one of the possible SVD decompositions • This is typically used for efficiency • The classic SVD is actually:
orthogonal Orthogonal
25AOctA16'74'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'75'
H'
Lecture 9 & 10 - Fei-Fei Li
What'we'will'learn'today?'
• IntroducIon'to'stereo'vision'• Epipolar'geometry:'a'gentle'intro'
• Parallel'images'&'image'recIficaIon'
• Solving'the'correspondence'problem'
• Homographic'transformaIon'
• AcIve'stereo'vision'system'
'
25AOctA16'76'
Reading:('[HZ]'Chapters:'4,'9,'11''[FP]'Chapters:'10 ''
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'77'
Ac5ve(stereo((point)'
Replace'one'of'the'two'cameras'by'a'projector'A 'Single'camera''A 'Projector'geometry'calibrated'A 'What’s'the'advantage'of'having'the'projector?''
P'
O2'
p’'
Correspondence'problem'solved!'
projector'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'78'
Ac5ve(stereo((stripe)''
O'A Projector'and'camera'are'parallel'A 'Correspondence'problem'solved!'
projector'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'79'
Ac5ve(stereo((shadows)''
Light'source' O'
A '1'camera,1'light'source'A 'very'cheap'setup'A 'calibrated'light'source'
J.'Bouguet'&'P.'Perona,'99'S.'Savarese,'J.'Bouguet'&'Perona,'00''
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'80'
Ac5ve(stereo((shadows)''J.'Bouguet'&'P.'Perona,'99'S.'Savarese,'J.'Bouguet'&'Perona,'00''
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'81'
Ac5ve(stereo((colorAcoded'stripes)''
A'Dense'reconstrucIon'''A 'Correspondence'problem'again'A 'Get'around'it'by'using'color'codes'
projector' O'
L.'Zhang,'B.'Curless,'and'S.'M.'Seitz'2002'
S.'Rusinkiewicz'&'Levoy'2002''
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'82'
Ac5ve(stereo((colorAcoded'stripes)''
L.'Zhang,'B.'Curless,'and'S.'M.'Seitz.'Rapid'Shape'AcquisiIon'Using'Color'Structured'Light'and'MulIApass'Dynamic'Programming.'3DPVT'2002'
Rapid'shape'acquisiIon:'Projector'+'stereo'cameras'
Lecture 9 & 10 - Fei-Fei Li 25AOctA16'83'
Ac5ve(stereo((stripe)''
• OpIcal'triangulaIon'– Project'a'single'stripe'of'laser'light'– Scan'it'across'the'surface'of'the'object'– This'is'a'very'precise'version'of'structured'light'scanning'
Digital Michelangelo Project http://graphics.stanford.edu/projects/mich/
Lecture 9 & 10 - Fei-Fei Li
Laser scanned models
The#Digital#Michelangelo#Project,'Levoy'et'al.'Slide credit: S. Seitz
25AOctA16'84'
Lecture 9 & 10 - Fei-Fei LiSlide credit: S. Seitz
25AOctA16'
The#Digital#Michelangelo#Project,'Levoy'et'al.'
Laser scanned models
85'
Lecture 9 & 10 - Fei-Fei LiSlide credit: S. Seitz
Laser scanned models
The#Digital#Michelangelo#Project,'Levoy'et'al.'
25AOctA16'86'
Lecture 9 & 10 - Fei-Fei LiSlide credit: S. Seitz
Laser scanned models
The#Digital#Michelangelo#Project,'Levoy'et'al.'
25AOctA16'87'
Lecture 9 & 10 - Fei-Fei Li
1.0'mm'resoluIon'(56'million'triangles)''
Slide credit: S. Seitz
Laser scanned models
The#Digital#Michelangelo#Project,'Levoy'et'al.'
25AOctA16'88'
Lecture 9 & 10 - Fei-Fei Li
What'we'have'learned'today'
• IntroducIon'to'stereo'vision'• Epipolar'geometry:'a'gentle'intro'
• Parallel'images'&'image'recIficaIon'
• Solving'the'correspondence'problem'
• Homographic'transformaIon'
• AcIve'stereo'vision'system'
'
25AOctA16'89'
Reading:('[HZ]'Chapters:'4,'9,'11''[FP]'Chapters:'10 ''