3D Vision

3D Computer Vision

and Video Computing 3D Vision3D Vision

Topic 3 of Part IIStereo Vision

CSc I6716Fall 2010

Zhigang Zhu, City College of New York zhu@cs.ccny.cuny.edu

3D Computer Vision

and Video Computing Stereo VisionStereo Vision

n Probleml Infer 3D structure of a scene from two or more images taken from

different viewpoints

n Two primary Sub-problemsl Correspondence problem (stereo match) -> disparity map

n “Similar” instead of “Same”n Occlusion problem: some parts of the scene are visible only in one eye

l Reconstruction problem -> 3Dn What we need to know about the cameras’ parametersn Often a stereo calibration problem

n Lectures on Stereo Visionl Stereo Geometry – Epipolar Geometry (*) l Correspondence Problem (*) – Two classes of approachesl 3D Reconstruction Problems – Three approaches

3D Computer Vision

and Video Computing A Stereo PairA Stereo Pair

n Problemsl Correspondence problem (stereo match) -> disparity mapl Reconstruction problem -> 3D

CMU CIL Stereo Dataset : Castle sequencehttp://www-2.cs.cmu.edu/afs/cs/project/cil/ftp/html/cil-ster.html

3D Computer Vision

and Video Computing More Images…More Images…

3D Computer Vision

and Video Computing Part I. Stereo GeometryPart I. Stereo Geometry

n A Simple Stereo Vision Systeml Disparity Equation l Depth Resolutionl Fixated Stereo System

n Zero-disparity Horopter

n Epipolar Geometryl Epipolar lines – Where to search correspondences

n Epipolar Plane, Epipolar Lines and Epipolesn http://www.ai.sri.com/~luong/research/Meta3DViewer/EpipolarGeo.html

l Essential Matrix and Fundamental Matrixn Computing E & F by the Eight-Point Algorithmn Computing the Epipoles

n Stereo Rectification

3D Computer Vision

and Video Computing Stereo GeometryStereo Geometry

n Converging Axes – Usual setup of human eyesn Depth obtained by triangulationn Correspondence problem: pl and pr correspond to the left and

right projections of P, respectively.

Object point

CentralProjection

Vergence Angle

P(X,Y,Z)

3D Computer Vision

and Video Computing A Simple Stereo SystemA Simple Stereo System

LEFT CAMERA

Left image:reference

Right image:target

RIGHT CAMERA

Elevation Zw

disparity

Depth Z

baseline

3D Computer Vision

and Video Computing Disparity EquationDisparity EquationP(X,Y,Z)

pl(xl,yl)

Optical Center Ol

f = focal length

Image plane

LEFT CAMERA

B = Baseline

Stereo system with parallel optical axes

f = focal length

Optical Center Or

pr(xr,yr)

Image plane

RIGHT CAMERA

Disparity: dx = xr - xl

3D Computer Vision

and Video Computing Disparity vs. BaselineDisparity vs. BaselineP(X,Y,Z)

pl(xl,yl)

Optical Center Ol

f = focal length

Image plane

LEFT CAMERA

B = Baseline

f = focal length

Optical Center Or

pr(xr,yr)

Image plane

RIGHT CAMERA

Disparity dx = xr - xl

Stereo system with parallel optical axes

3D Computer Vision

and Video Computing Depth AccuracyDepth Accuracyn Given the same image localization error

l Angle of cones in the figuren Depth Accuracy (Depth Resolution) vs.

Baselinel Depth Error 1/B (Baseline length)l PROS of Longer baseline,

n better depth estimationl CONS

n smaller common FOVn Correspondence harder due to occlusion

n Depth Accuracy (Depth Resolution) vs. Depthl Disparity (>0) 1/ Depthl Depth Error Depth2

l Nearer the point, better the depth estimation

n An Examplel f = 16 x 512/8 pixels, B = 0.5 ml Depth error vs. depth

Two viewpoints

Absolute error

Relative error

3D Computer Vision

and Video ComputingStereo with Converging CamerasStereo with Converging Cameras

n Stereo with Parallel Axes l Short baseline

n large common FOVn large depth error

l Long baselinen small depth errorn small common FOVn More occlusion problems

n Two optical axes intersect at the Fixation Pointl converging angle ql The common FOV Increases

Left right

3D Computer Vision

n Stereo with Parallel Axes l Short baseline

n large common FOVn large depth error

l Long baselinen small depth errorn small common FOVn More occlusion problems

Left right

3D Computer Vision

n Disparity propertiesl Disparity uses angle instead of

distancel Zero disparity at fixation point

n and the Zero-disparity horopterl Disparity increases with the distance

of objects from the fixation pointsn >0 : outside of the horoptern <0 : inside the horopter

n Depth Accuracy vs. Depthl Depth Error Depth2

Left right

Fixation point

3D Computer Vision

and Video Computing

Stereo with Converging CamerasStereo with Converging Cameras

Left right

Fixation point

ar = al

d a = 0

Horopter

3D Computer Vision

and Video Computing

Stereo with Converging CamerasStereo with Converging Cameras

Left right

Fixation point

ar > al

d a > 0

Horopter

3D Computer Vision

Left right

Fixation point

ar < al

d a < 0

Horopter

3D Computer Vision

Left right

Fixation point

(D d ) a?

Horopter

3D Computer Vision

and Video Computing BreakBreak

n Homework #4 online, due on November 29 before class

3D Computer Vision

and Video Computing Parameters of a Stereo SystemParameters of a Stereo System

n Intrinsic Parametersl Characterize the

transformation from camera to pixel coordinate systems of each camera

l Focal length, image center, aspect ratio

n Extrinsic parametersl Describe the relative

position and orientation of the two cameras

l Rotation matrix R and translation vector T

3D Computer Vision

and Video Computing Epipolar GeometryEpipolar Geometry

n Notations

l Pl =(Xl, Yl, Zl), Pr =(Xr, Yr, Zr) n Vectors of the same 3-D point

P, in the left and right camera coordinate systems respectively

l Extrinsic Parametersn Translation Vector T = (Or-Ol) n Rotation Matrix R

l pl =(xl, yl, zl), pr =(xr, yr, zr)n Projections of P on the left and

right image plane respectivelyn For all image points, we have

zl=fl, zr=fr

T)R(PP lr

3D Computer Vision

and Video Computing Epipolar GeometryEpipolar Geometryn Motivation: where to search

correspondences?l Epipolar Plane

n A plane going through point P and the centers of projections (COPs) of the two cameras

l Conjugated Epipolar Lines n Lines where epipolar plane

intersects the image planes

l Epipolesn The image of the COP of one

camera in the othern Epipolar Constraint

l Corresponding points must lie on conjugated epipolar lines

Ol Orel er

Epipolar Plane

Epipolar Lines

Epipoles

3D Computer Vision

and Video Computing Essential MatrixEssential Matrix

n Equation of the epipolar planel Co-planarity condition of vectors Pl, T and Pl-T

n Essential Matrix E = RS l 3x3 matrix constructed from R and T (extrinsic only)

n Rank (E) = 2, two equal nonzero singular values

0 ll PTT)(P T

333231

232221

131211

Rank (R) =3 Rank (S) =2

T)R(PP lr

0lTr EPP

0lTr Epp

3D Computer Vision

and Video Computing Essential MatrixEssential Matrix

n Essential Matrix E = RS l A natural link between the stereo point pair and the

extrinsic parameters of the stereo system n One correspondence -> a linear equation of 9 entriesn Given 8 pairs of (pl, pr) -> E

l Mapping between points and epipolar lines we are looking forn Given pl, E -> pr on the projective line in the right planen Equation represents the epipolar line of pr (or pl) in the

right (or left) image

n Note: l pl, pr are in the camera coordinate system, not pixel

coordinates that we can measure

0lTr Epp

3D Computer Vision

and Video Computing Fundamental MatrixFundamental Matrix

n Mapping between points and epipolar lines in the pixel coordinate systemsl With no prior knowledge on the stereo system

n From Camera to Pixels: Matrices of intrinsic parameters

n Questions: l What are fx, fy, ox, oy ?l How to measure pl in images?

0lTr pFp

1 lr EMMF T

l1ll pMp rrr pMp 1

int yy

Tr Epp

Rank (Mint) =3

3D Computer Vision

and Video Computing Fundamental MatrixFundamental Matrix

n Fundamental Matrix l Rank (F) = 2l Encodes info on both intrinsic and extrinsic parameters

l Enables full reconstruction of the epipolar geometryl In pixel coordinate systems without any knowledge of

the intrinsic and extrinsic parameters l Linear equation of the 9 entries of F

0lTr pFp

1 lr EMMF T

1333231

232221

131211

)1( )(

3D Computer Vision

and Video ComputingComputing F: The Eight-point AlgorithmComputing F: The Eight-point Algorithm

n Input: n point correspondences ( n >= 8)l Construct homogeneous system Ax= 0 from

n x = (f11,f12, ,f13, f21,f22,f23 f31,f32, f33) : entries in Fn Each correspondence give one equationn A is a nx9 matrix

l Obtain estimate F^ by SVD of An x (up to a scale) is column of V corresponding to the least

singular valuel Enforce singularity constraint: since Rank (F) = 2

n Compute SVD of F^n Set the smallest singular value to 0: D -> D’n Correct estimate of F :

n Output: the estimate of the fundamental matrix, F’n Similarly we can compute E given intrinsic parameters

0lTr pFp

TUDVF ˆ

TVUDF' '

3D Computer Vision

and Video ComputingLocating the Epipoles from FLocating the Epipoles from F

n Input: Fundamental Matrix Fl Find the SVD of Fl The epipole el is the column of V corresponding to the

null singular value (as shown above)l The epipole er is the column of U corresponding to the

null singular valuen Output: Epipole el and er

el lies on all the epipolar lines of the left image

0lTr pFp

0lTr eFp

F is not identically zero

For every pr

Ol Orel er

Epipolar Plane

Epipolar Lines

Epipoles

3D Computer Vision

and Video Computing Stereo RectificationStereo Rectification

n Rectification l Given a stereo pair, the intrinsic and extrinsic parameters, find

the image transformation to achieve a stereo system of horizontal epipolar lines

l A simple algorithm: Assuming calibrated stereo cameras

p’lp’r

Y’l Y’r

TX’l

n Stereo System with Parallel Optical Axesn Epipoles are at infinityn Horizontal epipolar lines

3D Computer Vision

n Algorithml Rotate both left and

right camera so that they share the same X axis : Or-Ol = T

l Define a rotation matrix Rrect for the left camera

l Rotation Matrix for the right camera is RrectRT

l Rotation can be implemented by image transformation

TX’l

Xl’ = T, Yl’ = Xl’xZl, Z’l = Xl’xYl’

3D Computer Vision

TX’l

Xl’ = T, Yl’ = Xl’xZl, Z’l = Xl’xYl’

3D Computer Vision

p’lp’r

Y’l Y’r

TX’l

T’ = (B, 0, 0), P’r = P’l – T’

3D Computer Vision

and Video Computing Epipolar Geometry: SummaryEpipolar Geometry: Summary

n Purposel where to search correspondences

n Epipolar plane, epipolar lines, and epipoles l known intrinsic (f) and extrinsic (R, T)

n co-planarity equation l known intrinsic but unknown extrinsic

n essential matrixl unknown intrinsic and extrinsic

n fundamental matrix

n Rectificationl Generate stereo pair (by software) with parallel optical

axis and thus horizontal epipolar lines

0lTr Epp

0lTr pFp

r PTRP

3D Computer Vision

and Video Computing Part II. Correspondence problemPart II. Correspondence problem

n Three Questionsl What to match?

n Features: point, line, area, structure?l Where to search correspondence?

n Epipolar line?l How to measure similarity?

n Depends on featuresn Approaches

l Correlation-based approachl Feature-based approach

n Advanced Topicsl Image filtering to handle illumination changesl Adaptive windows to deal with multiple disparitiesl Local warping to account for perspective distortionl Sub-pixel matching to improve accuracyl Self-consistency to reduce false matchesl Multi-baseline stereo

3D Computer Vision

and Video Computing Correlation ApproachCorrelation Approach

n For Each point (xl, yl) in the left image, define a window centered at the point

(xl, yl)LEFT IMAGE

3D Computer Vision

n … search its corresponding point within a search region in the right image

(xl, yl)RIGHT IMAGE

3D Computer Vision

n … the disparity (dx, dy) is the displacement when the correlation is maximum

(xl, yl)dx(xr, yr)RIGHT IMAGE

3D Computer Vision

n Elements to be matchedl Image window of fixed size centered at each pixel in the

left imagen Similarity criterion

l A measure of similarity between windows in the two images

l The corresponding element is given by window that maximizes the similarity criterion within a search region

n Search regionsl Theoretically, search region can be reduced to a 1-D

segment, along the epipolar line, and within the disparity range.

l In practice, search a slightly larger region due to errors in calibration

3D Computer Vision

n Equations

n disparity

n Similarity criterion l Cross-Correlation

l Sum of Square Difference (SSD)

l Sum of Absolute Difference(SAD)

Wlllrlll ldyykdxxIlykxIdydxc )),(),,((),(

)},({maxarg),( dydxcydxdR

uvvu ),(

2)(),( vuvu

||),( vuvu

3D Computer Vision

n PROSl Easy to implementl Produces dense disparity mapl Maybe slow

n CONSl Needs textured images to work well l Inadequate for matching image pairs from very different

viewpoints due to illumination changesl Window may cover points with quite different disparitiesl Inaccurate disparities on the occluding boundaries

3D Computer Vision

n A Stereo Pair of UMass Campus – texture, boundaries and occlusion

3D Computer Vision

and Video Computing Feature-based ApproachFeature-based Approach

n Featuresl Edge pointsl Lines (length, orientation, average contrast)l Corners

n Matching algorithml Extract features in the stereo pairl Define similarity measurel Search correspondences using similarity measure and

the epipolar geometry

3D Computer Vision

n For each feature in the left image…

LEFT IMAGE

corner line

structure

3D Computer Vision

n Search in the right image… the disparity (dx, dy) is the displacement when the similarity measure is maximum

RIGHT IMAGE

corner line

structure

3D Computer Vision

n PROSl Relatively insensitive to illumination changesl Good for man-made scenes with strong lines but weak

texture or textureless surfacesl Work well on the occluding boundaries (edges)l Could be faster than the correlation approach

n CONSl Only sparse depth mapl Feature extraction may be tricky

n Lines (Edges) might be partially extracted in one imagen How to measure the similarity between two lines?

3D Computer Vision

and Video Computing Advanced TopicsAdvanced Topics

n Mainly used in correlation-based approach, but can be applied to feature-based match

n Image filtering to handle illumination changes

l Image equalizationn To make two images more similar in illumination

l Laplacian filtering (2nd order derivative)n Use derivative rather than intensity (or original color)

3D Computer Vision

n Adaptive windows to deal with multiple disparitiesl Adaptive Window Approach (Kanade and Okutomi)

n statistically adaptive technique which selects at each pixel the window size that minimizes the uncertainty in disparity estimates

n A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment, T. Kanade and M. Okutomi. Proc. 1991 IEEE International Conference on Robotics and Automation, Vol. 2, April, 1991, pp. 1088-1095

l Multiple window algorithm (Fusiello, et al)n Use 9 windows instead of just one to compute the SSD

measuren The point with the smallest SSD error amongst the 9

windows and various search locations is chosen as the best estimate for the given points

n A Fusiello, V. Roberto and E. Trucco, Efficient stereo with multiple windowing, IEEE CVPR pp858-863, 1997

3D Computer Vision

n Multiple windows to deal with multiple disparities

Smooth

regions

Corners

near far

3D Computer Vision

n Sub-pixel matching to improve accuracyl Find the peak in the correlation curves

n Self-consistency to reduce false matches esp. for occlusionsl Check the consistency of matches from L to R and from R to L

n Multiple Resolution Approachl From coarse to fine for efficiency in searching correspondences

n Local warping to account for perspective distortionl Warp from one view to the other for a small patch given an initial

estimation of the (planar) surface normal

n Multi-baseline Stereol Improves both correspondences and 3D estimation by using more than

two cameras (images)

3D Computer Vision

and Video Computing 3D Reconstruction Problem3D Reconstruction Problem

n What we have donel Correspondences using either correlation or feature

based approachesl Epipolar Geometry from at least 8 point

correspondencesn Three cases of 3D reconstruction depending on the

amount of a priori knowledge on the stereo systeml Both intrinsic and extrinsic known - > can solve the

reconstruction problem unambiguously by triangulationl Only intrinsic known -> recovery structure and extrinsic

up to an unknown scaling factorl Only correspondences -> reconstruction only up to an

unknown, global projective transformation (*)

3D Computer Vision

and Video ComputingReconstruction by TriangulationReconstruction by Triangulation

n Assumption and Probleml Under the assumption that both

intrinsic and extrinsic parameters are known

l Compute the 3-D location from their projections, pl and pr

n Solutionl Triangulation: Two rays are

known and the intersection can be computed

l Problem: Two rays will not actually intersect in space due to errors in calibration and correspondences, and pixelization

l Solution: find a point in space with minimum distance from both rays

3D Computer Vision

and Video ComputingReconstruction up to a Scale FactorReconstruction up to a Scale Factor

n Assumption and Problem Statementl Under the assumption that only intrinsic parameters and

more than 8 point correspondences are givenl Compute the 3-D location from their projections, pl and pr, as

well as the extrinsic parametersn Solution

l Compute the essential matrix E from at least 8 correspondences

l Estimate T (up to a scale and a sign) from E (=RS) using the orthogonal constraint of R, and then R n End up with four different estimates of the pair (T, R)

l Reconstruct the depth of each point, and pick up the correct sign of R and T.

l Results: reconstructed 3D points (up to a common scale);l The scale can be determined if distance of two points (in

space) are known

3D Computer Vision

and Video ComputingReconstruction up to a Projective TransformationReconstruction up to a Projective Transformation

n Assumption and Problem Statementl Under the assumption that only n (>=8) point

correspondences are givenl Compute the 3-D location from their projections, pl and

prn Solution

l Compute the Fundamental matrix F from at least 8 correspondences, and the two epipoles

l Determine the projection matrices n Select five points ( from correspondence pairs) as the

projective basisl Compute the projective reconstruction

n Unique up to the unknown projective transformation fixed by the choice of the five points

(* not required for this course; needs advanced knowledge of projective geometry )

3D Computer Vision

and Video Computing SummarySummary

n Fundamental concepts and problems of stereon Epipolar geometry and stereo rectificationn Estimation of fundamental matrix from 8 point pairsn Correspondence problem and two techniques:

correlation and feature based matchingn Reconstruct 3-D structure from image

correspondences givenl Fully calibratedl Partially calibration l Uncalibrated stereo cameras (*)

3D Computer Vision

and Video Computing NextNext

n Understanding 3D structure and events from motion

Motion

3D Vision

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