נתחיל בסגירת חוב... geometric vision goal: recovery of 3d structure – structure and...
TRANSCRIPT
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Presented by: Hadar Elor
Stereo Vision
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Geometric vision• Goal: Recovery of 3D structure
– Structure and depth are inherently ambiguous from single views.
הסנר טל של השקפים על מבוסס
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Geometric vision• Goal: Recovery of 3D structure
– What cues in the image allow us to do this?
Slide credit: Svetlana Lazebnik
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Shading
[Figure from Prados & Faugeras 2006]
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Focus/defocus
[figs from H. Jin and P. Favaro, 2002]
Images from same point of view, different camera parameters
3d shape / depth estimates
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Texture
[From A.M. Loh. The recovery of 3-D structure using visual texture patterns. PhD thesis]
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Perspective effects
Image credit: S. Seitz
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Motion
Figures from L. Zhang http://www.brainconnection.com/teasers/?main=illusion/motion-shape
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Estimating scene shape • “Shape from X”: Shading, Texture, Focus, Motion…
• Stereo: – shape from “motion” between two views– infer 3d shape of scene from two (multiple) images
from different viewpoints
scene point
optical center
image plane
Main idea:
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Geometry for a Simple Stereo System
• First, assuming parallel optical axes, known camera parameters (i.e., calibrated cameras):
11B. LeibeSlide credit: Kristen Grauman
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12B. Leibe
baseline
optical center (left)
optical Center (right)
Focal length
World point
Depth of pimage point (left) image point
(right)
Slide credit: Kristen Grauman
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Geometry for a Simple Stereo System• Assume parallel optical axes, known camera
parameters (i.e., calibrated cameras). We can triangulate via:
13B. Leibe
Similar triangles (pl, P, pr) and (Ol, P, Or):
r lT x x T
Z f Z
lr xx
TfZ
disparity
Slide credit: Kristen Grauman
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Disparity ועומק
מצלמות
הבדל במיקומי הנקודה (disparityבתמונות )
( ת
מוצל
מה
מק
חמר
dept
h)
lr xx
TfZ
disparity
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Depth From Disparity
15B. Leibe
Image I(x,y) Image I´(x´,y´)Disparity map D(x,y)
(x´,y´)=(x+D(x,y), y)
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General Case With Calibrated Cameras
• The two cameras need not have parallel optical axes.
16B. Leibe
vs.
Slide credit: Kristen Grauman, Steve Seitz
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Stereo Correspondence Constraints
• Given p in the left image, where can the corresponding point p’ in the right image be?
17B. LeibeSlide credit: Kristen Grauman
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Stereo Correspondence Constraints
• Given p in the left image, where can the corresponding point p’ in the right image be?
18B. LeibeSlide credit: Kristen Grauman
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Stereo Correspondence Constraints
19B. LeibeSlide credit: Kristen Grauman
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Stereo Correspondence Constraints
• Geometry of two views allows us to constrain where the corresponding pixel for some image point in the first view must occur in the second view.
• Epipolar constraint: Why is this useful?– Reduces correspondence problem to 1D search along conjugate
epipolar lines.
20B. Leibe
epipolar planeepipolar lineepipolar line
Slide adapted from Steve Seitz
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Epipolar Geometry
21
• Epipolar Plane
• Epipoles • Epipolar Lines
• Baseline
Slide adapted from Marc Pollefeys
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Example
23B. LeibeSlide credit: Kristen Grauman
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• For a given stereo rig, how do we express the epipolar constraints algebraically?
24B. Leibe
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ההכרחית המטריצה בניית
נגדיר •סיבוב • מטריצת P, הקשר בין מיקום Rעבור
במערכת הקואורדינטות השמאלית לימנית הוא:
lO rO
P
l rlp
rp
מישור פולרי- י אפ�
r l T O O
r l P R P T
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Rotation MatrixExpress 3d rotation as series of rotations around coordinate axes by angles
,,
Overall rotation is product of these elementary rotations:
zyx RRRR
Slide credit: Kristen Grauman
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ההכרחית המטריצה בניית
• - על , נמצאים ו הוקטורים שלושת : האפיפולרי המישור המישור אותו
lO rO
P
l rlp
rp
מישור פול- י אפ�רי
lPT) )l P T
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Cross Product & Dot Product
• Vector cross product takes two vectors and returns a third vector that’s perpendicular to both inputs.
• So here, c is perpendicular to both a and b, which means the dot product = 0.
Slide credit: Kristen Grauman
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ההכרחית המטריצה בניית
• - על , נמצאים ו הוקטורים שלושת : האפיפולרי המישור המישור אותו
למישור • הניצב וקטור הוא מכאן:••- ו היותאזי:•• : ונקבל במשואה נציב
lO rO
P
l rlp
rp
מישור פול- י אפ�רי
lPT) )l P T
lT P
0T
l l P T T P
r l P R P T
Tr l R P P T
0TT T
r l r l R P T P P RT P
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Matrix Form of Cross Product
30Slide credit: Kristen Grauman
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ההכרחית המטריצה בניית
מטריצות • כפל באמצעות נשכתב– נגדיר –ונקבל: –
נקראת Eהמטריצה •המטריצה ההכרחית
)Essential Matrix)
0TT T
r l r l R P T P P RT P
0TT T
r l r x l R P T P P R T P
xE R T
0Tr l P EP
lO rO
P
l rlp
rp
מישור פול- י אפ�רי
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ההכרחית המטריצה
נתונות • התמונות במישורי ונקודות היות ,' נחליף הומ (שכן זהות p ב- Pבקואורדינטות
עד לכדי כפל בקבוע(ישר במישור התמונה הימנית אשר הוא •
מובטח כי מכיל את הנקודה•E שימושית כאשר נתונות לנו קואורדינטות נקודות
התמונה.במישורלנו יש קואורדינטות פיקסלים בתמונה...•
0Tr l p Ep
r lu Ep
rp
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היסודית המטריצה
היסודית • Fundamental Matrix Fמטריצהדומה באופייה למטריצה היסודית אך הפעם ו- •
פיקסליםבקואורדינטות • עבור ו- מטריצות פנימיות של שתי •
המצלמותאלג' שמונה חישוב המטריצה היסודית באמצעות "•
"הנקודות
0Tr l p Fp
lprp
1Tr l F M EM
lMlM
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Fundamental matrix
• Relates pixel coordinates in the two views
• More general form than essential matrix: we remove need to know intrinsic parameters
• If we estimate fundamental matrix from correspondences in pixel coordinates, can reconstruct epipolar geometry without intrinsic or extrinsic parameters
Grauman
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Computing F from correspondences
• Cameras are uncalibrated: we don’t know E or left or right Mint matrices
• Estimate F from 8+ point correspondences.
0leftright pFp
1int.int,
leftright EMMF
Grauman
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Computing F from correspondences
0leftright pFp
Each point correspondence generates one constraint on F
Grauman
We can re-write as:
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סטראו מערכת כיול
So, where to start with uncalibrated cameras?Need to find fundamental matrix F and the correspondences (pairs of points (u’,v’) ↔
(u,v)).
1) Find interest points in image2) Compute correspondences3) Compute epipolar geometry4) Refine
Example from Andrew Zisserman
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1) Find interest points
Stereo pipeline with weak calibration
Grauman
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2) Match points only using proximity
Stereo pipeline with weak calibration
Grauman
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Putative matches based on correlation search
Grauman
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עעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעעע
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RANSAC for robust estimation of the fundamental matrix
• Select random sample of correspondences
• Compute F using them– This determines epipolar constraint
• Evaluate amount of support – inliers within threshold distance of epipolar line
• Choose F with most support (inliers)
Grauman
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Putative matches based on correlation search
Grauman
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Pruned matches• Correspondences consistent with epipolar geometry
Grauman
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• Resulting epipolar geometry
Grauman
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? היום ראינו מה אז
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סיכום
סטראו • מערכת של אנטומיהאפיפולרית • גיאומטריה
– , האפיפולרי, המישור האפיפולרי הישר האפיפול
• ' , מע וכיול היסודית ההכרחית המטריצהסטראו
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שקפים מקורות
• , על מבוססים רבים שקפים שצוין כל מלבד: של אלו
–B. Leibe–K. Grauman– D. Low–S. Lazebnik–A. Torralba–T. Darrell