立体视觉：算法与应用（英文版）

StereoVision:AlgorithmsandApplicationsStefanoMattocciaDepartmentofComputerScience

(DISI)UniversityofBolognastefano.mattoccia@unibo.itwww.vision.deis.unibo.it/smattJanuary

12,2013Stefano

MattocciaUpdates•Jan2015:

added

new

3D

applications•Addeddetailsaboutourstereocamerawith

FPGAprocessing•November21,2011:addedexperimental

results

for

“Linearstereomatching”

(ICCV2011),Minetal’salgorithm

(ICCV2011),

descriptionof“FastSegmentationdriven

(FSD)”

(IC3D)algorithmanddescriptionofSGM•May19,2011:addedexperimental

results

of

FBS

on

the

GPU[71]andtheVisionSt

stereo

camera•July25,2010:Linux

andWindows

implementations

of

theFastBilateralStereoalgorithmavailableat:www.vision.deis.unibo.it/smatt/fast_bilateral_stereo.htm•April20th

,2010:includeddescriptions

andexperimental

resultsforpapers

[67],

[68],

[69]Stefano

MattocciaOutline•

Introductionto

stereovision•

Overviewofa

stereovision

system•Algorithmsforvisual

correspondence•

Computationaloptimizations•

Hardwareimplementation•

ApplicationsStefano

MattocciaWhatis

stereovision

?•

Isatechniqueaimed

at

inferring

depth

fromtwoormore

cameras•Wideresearch

topic

in

computervision•

Thisseminar

isconcernedwith•binocular

stereovision

systems•

densestereo

algorithms•

stereovisionapplications•

Emphasisison

approaches

that

are

(ormightbehopefullysoon)feasible

forreal-time/hardware

implementationStefano

Mattocciawww.nasa.gov

www.vision.deis.unibo.it/smatt/stereoApplicationsStefano

MattocciaQπp≡qP•

Both

(real)points

(PandQ)

projectinto

the

same

imagepoint

(p≡

q)•

Thisoccurs

foreachpoint

alongthe

same

line

of

sight•Useful

for

optical

illusions…Oπ:image

planeO:optical

centerSinglecameraCourtesyofhttp://www.coolopti/Stefano

MattocciaStereocamera

ReferenceRTargetTWithtwo

(ormore)cameraswecan

inferdepth,bymeans

oftriangulation,ifweareable

to

findcorresponding(homologous)pointsinthe

two

imagesStefano

Mattoccia?Howtosolve

the

correspondenceproblem

?No!!Thanks

to

theepipolarconstraintReference

(R)Target

(T)Reference

(R)Target

(T)2D

searchdomain

?Stefano

MattocciaQπRπTp≡q‘q‘vOR

OTReferenceR

TargetT•

ConsidertwopointsPandQ

on

the

same

line

of

sight

of

thereferenceimageR

(bothpointsprojectinto

the

same

imagepointp≡qonimageplaneπR

ofthereferenceimage)•

Theepipolarconstraintstatesthat

the

correspondence

for

apointbelongingtothe

(red)

lineof

sight

lies

on

theEpipolarconstraintPgreenlineon

imageplaneπT

oftarget

imageStefano

MattocciapπTpq‘yOTTarget

Tp≡qORReferenceRQπRyPOnceweknowthatthesearch

space

for

correspondingpoints

canbenarrowedfrom2Dto

1D,we

canput

(virtually)

thestereoriginamoreconvenient

configuration

(standard

form)-correspondingpointsare

constrainedon

the

same

image

scanlineStereocamerain

standard

formStefano

Mattoccia‘Stereopairin

standardformCamerasare“perfectly”alignedandwiththe

same

focal

lengthOriginalstereopairStefano

Mattocciap

Z

p’xR

xTOR

OTB

(Baseline)fP

xR

_

xT

is

the

disparityWiththestereorig

in

standard

formandby

considering

similartriangles

(POROT

andPpp’):DisparityanddepthStefano

MattocciaDisparityanddepthStefano

MattocciaxTDisparitymapPpxRORp’xTOTBPp

Z

p’xR

xTOR

OTBThedisparityisthedifferencebetween

thex

coordinate

oftwocorrespondingpoints;itis

typicallyencodedwithgreyscaleimage

(closerpointsarebrighter).xR(Baseline)

(Baseline)

Stefano

MattocciaDisparityishigherforpointscloser

to

the

camerav

ReferenceTargetZRangefield

(Horopter)Givenastereorigwithbaselineband

focal

length

f,

the

rangefieldofthe

systemis

constrainedby

thedisparity

range

[dmin

,

dmax].Horopterb

.

fdminb

.

fdmaxStefano

Mattocciab

.

fdmin

b

.

fdmaxintoparallelplanes

(oneforeachdisparityvalue)•Abetter

(virtual)discretizationcanbeachievedwithsubpixeltechniques

(seeDisparityRefinements)

Stefano

Mattoccia•Depthmeasuredbyastereovisionsystemis

discretizedb

.

fdmin

b

.

fdmin

+

4•Therangefield

(horopter)using

5

disparityvalues[dmin

,

dmin+4]Stefano

Mattocciab

.

fΔ

+

dmin

b

.

fΔ

+

dmin

+

4min

,

min•WithΔ>0,horoptergetscloserand

shrinks

(depth

andobviouslyarea/volume)•Using5disparity

values

[Δ+d

Δ+d

+4]Stefano

MattocciaAdd

here

!Accuracy

vs

Resolution:quantitative

analysis

bStefano

MattocciaColororgreyscale

sensors

?InserthereStefano

MattocciaKeymoduleinstereovision?Thealgorithmiscrucial

in

this

technologyStateof

the

art

(e.g.

ICCV2011)Traditional

algorithmStereopairRectifiedstereopairDisparitymapDepthmapRectificationStereoCorrespondenceTriangulationPC,

FPGAIntrinsicand

extrinsicparametersCalibration

(offline)Overviewofa

stereovision

systemStefano

Mattoccia•Intrinsicparametersof

the

two

cameras

(focallength,image

center,parametersof

lenses

distortion,etc)•Extrinsicparameters(RandT

thataligns

the

two

cameras)

Calibration

(offline)

Offlineprocedureaimedatfinding:Calibration

(offline)RectificationStereoCorrespondenceTriangulationCalibrationiscarriedout

acquiring

andprocessing10+stereopairs

of

a

knownpattern

(typicallyacheckerboard)Stefano

Mattoccia•Calibrationisavailable

inOpenCV

[39]

and

Matlab

[40]•Adetaileddescriptionofcalibrationcanbe

foundin

[20,21,22]•Nextslidesshow

20

stereopairs

used

for

calibratingastereo

cameraCalibration

(offline)RectificationStereoCorrespondenceTriangulationStefano

MattocciaRTRTStefano

MattocciaRTRTStefano

Mattoccia

Rectification

Usingtheinformationfrom

the

calibration

step:Calibration

(offline)RectificationStereoCorrespondenceTriangulationa)removeslens

distortionsb)turnsthe

stereopair

in

standard

formStereocamera

instandardformStefano

MattocciaCalibration

(offline)RectificationStereoCorrespondenceTriangulationdisparitymapThistopicwillbeextensivelyanalyzedinthe

next

slides...StereocorrespondenceAimsatfindinghomologouspoints

in

thestereo

pair.Stefano

MattocciaGiventhedisparitymap,thebaselineand

theFocallength

(calibration):triangulation

computesthepositionofthecorrespondence

in

the

3DCalibration

(offline)RectificationStereoCorrespondenceTriangulationdepthmapTriangulationspace

-"'d(x,y)

X

=

Z

xRf

,(X,Y,Z)disparitymapStefano

MattocciaDatasets:stereo

sequencesSequencesacquiredwithstereocamerasareavailable

at:http://www.vision.deis.unibo.it/smatt/stereo.htmThedatasetsinclude:•

calibrationparameters•

originalsequences•

rectifiedsequences•

disparitymapsStefano

MattocciaArchitectures•Microprocessors-FloatingPoint

(FP)

units

+

SIMD-C/C++

(+

assembly)-power,costandsizeare

themaindrawbacks•

Lowpower&

low

costprocessor-

C/c++-

no

FP-no

SIMD

(often)•

GPUs

(GraphicProcessingUnits)–

rawpower-highpowerdissipationandcost-programmingisdifficult

(CUDAandOpenCLhelp)•

FPGA

(FieldProgrammableGateArray)-efficient,lowpower

(<1W),

low

cost-programminglanguage:VHDL-codingisdifficultand

tailored

for

specificdevicesStefano

Mattoccia•Wehavedesignedareal-timestereo

camera

withdepthmapscomputedaccordingto

stateof

the

art

algorithms•Details:www.vision.deis.unibo.it/smatt•Youtube

channel:/channel/UChkayQwiHJuf3nqMikhxAlwOurcustomFPGA-basedstereo

camera

1/3•Processingat

30+

fps

(640x480)•Powerconsumption:<

2.5Watt•SelfpoweredviaUSB

cable•Weight:<80gwith

lenses

andholdersOurcustomFPGA-basedstereo

camera

2/3OurcustomFPGA-basedstereo

camera

3/3www.youtube.com/watch?v=KXFWIvrcAYoSomeavailablestereo

camerasFPGA/DSAS

P

wwww.minoru3dwwww.videredewww.valdesyswww.visionwww.ptgreStefano

MattocciaIC*Whyisstereocorrespondence

so

challenging

?Next

slides

show

commonpitfalls…Stefano

MattocciaPhotometricdistortionsandnoiseSpecularsurfacesStefano

MattocciaUniquenessconstraint

?

:-

(Stefano

MattocciaForeshortening??????Uniform/ambiguousregionsdistortionsPerspectiveStefano

MattocciaRepetitive/ambiguouspatternsHowto

reduceambiguity...

?Stefano

Mattoccia?Occlusionsanddiscontinuities1/2TransparentobjectsStefano

MattocciaReference

Target??Occlusions

and

discontinuities2/2TargetReferenceStefano

MattocciaMiddlebury

stereo

evaluationTheMiddleburystereoevaluationsite

[15]providesa

framework

andadataset

(showedinthenext

slide)

forbenchmarkingnovel

algorithms.ScharsteinandSzeliskiprovide:•amethodologyforthe

evaluation

of

(binocular)

stereovisionalgorithms

[11]•datasetswithgroundtruth

[11,15,17,18,19]•onlineevaluationprocedureandranking

[15]Datasets

(withgroundtruth)ofstereopairsaffectedbyphotometricdistortionsarealsoavailablein

[15].[15]

D.Scharsteinand

R.Szeliski,

/stereo/eval/[11]

D.Scharsteinand

R.Szeliski,“Ataxonomyandevaluationofdensetwo-framestereo

correspondence

algorithms

M

Int.Jour.ComputerVision,47(1/2/3):7–42,2002Stefano

MattocciaLeft

Right

Groundtruth

ALLDISC

NON_OCCMiddleburydataset

(2003)

[15]Tsukuba,Venus,TeddyandConesstereopairsD.Scharsteinand

R.Szeliski,http:///stereo/eval/Stefano

MattocciaThecorrespondenceproblemAccordingtothetaxonomyproposed

in

[11]most

stereo

algorithmsperform

(subsetof)these

steps:1)Matchingcostcomputation2)Costaggregation3)Disparitycomputation/optimization4)DisparityrefinementLocalalgorithmsperform:1

→2

→3

(witha

simpleWinner

TakesAll

(WTA)

strategy)GlobalAlgorithmsperform:1

(

→2)

→

3

(withglobalor

semi-global

reasoning)Stefano

MattocciaPre-processing

(0)Sometimeisdeployedapre-processingstagemainly

tocompensate

for

photometric

distortions.Typicaloperationsinclude:•LaplacianofGaussian

(LoG)

filtering

[41]•Subtractionofmeanvaluescomputedin

nearbypixels

[42]•Bilateralfiltering

[16]•Census

transform[41]T.

Kanade,

H.

Kato,S.

Kimura,

A.Yoshida,and

K.Oda,

DevelopmentofaVideo-RateStereo

MachineInternational

RoboticsandSystemsConference(IROS'95),

Human

Robot

Interaction

and

Cooperative

Robots,

1995[42]O.

Faugeras,

B.

Hotz,

H.

Mathieu,T.Viville,Z.Zhang,

P.

Fua,

E.Thron,

L.

Moll,G.

Berry,Real-timecorrelation-basedstereo:

Algorithm.

Implementationand

Applications,

INRIA

TRn.2013,

1993[16]

A.

Ansar,

A.Castano,

L.

Matthies,

Enhancedreal-timestereousing

bilateralfilteringIEEEConferenceonComputerVisionand

Pattern

Recognition2004Stefano

MattocciaHHyepipolar

lineReference

(R)

Target

(T)W

Wx

x

x+dmaxThesimplest

(naiveandunused)local

approach:Reference

(R)Target

(T)Stefano

Mattoccia0

dmaxd(x,

y)

一

IT

(x

+

d,

y)•matchingcost

(1):pixel-basedabsolutedifferencebetween

pixel

intensities•disparitycomputation

(3):WinnerTakesAll

(WTA)Result(disappointing)GroundtruthReferenceStefano

MattocciaWinner

d*IR•

Global

(andsemi-global*)algorithmssearchfor

disparityassignmentsthatminimizeanenergyfunctionover

thewhole

stereopairusingapixel-basedmatchingcost

(sometimethematchingcostisaggregatedover

a

support).

Steps

1+3Bothapproachesassumethat

the

scene

ispiecewisesmooth.Sometime

this

assumption

is

violated...Thishypothesisisimplicitlyassumedby

localapproacheswhileitisexplicitlymodelledbyHowtoimprove

theresults

of

the

naive

approach

?Basicallyexisttwodifferent

(notmutuallyexclusive)

strategies:•

LocalalgorithmsusethesimpleWTAdisparity

selection

strategybutreduceambiguity

(increasingthe

signal

to

noiseratio

(SNR))byaggregatingmatchingcostsoverasupportwindow

(akakernelorcorrelationwindow).Sometimeasmoothnessterm

is

adopted.

Steps

1+2

(+WTA)globalapproaches*subset

of

the

stereopairStefano

MattocciaGlobal

(andsemi-global*)approaches:Manyalgorithmssearchfor

thedisparity

assignment

thatminimizeacertaincostfunction

over

thewhole*

stereoLocalapproaches:Inordertoincrease

the

SNR

(reduce

ambiguity)

thematchingcostsareaggregatedovera

supportwindow*

subset

of

the

stereo

pair

Stefano

MattocciaE(d)=

Edata

(d

)+

Esmooth

(d

)Reference

(R)Target

(T)pair•Squareddifferencese(x,

y,

d)=(IR

(x,

y)一

IT

(x+

d

,

y))2•Robustmatchingmeasures

(M-estimators)•Limitinfluence

of

outliers•Example:truncatedabsolutedifferences

(TAD)e(x,

y,

d)=

min{IR

(x,

y)

一

IT

(x

+

d

,

y)

,

T}Pixel-basedmatchingcosts•Absolutedifferencese(x,

y,

d)=IR

(x,

y)

一

IT

(x

+

d,

y)IR

(x,y)

IT

(x,y)□

□R

TMatchingcostcomputation

(1)Stefano

Mattoccia

(xR

+

d

,

y)IT

(xR

+

d,

y)xR

+

d_

1

xR

+

d

+1x

+

dI

T~IR

(xR

,

y)xR

_

1

xR

+1x

(xR

,

y)IR~

•Dissimilaritymeasureinsensitivetoimage

sampling

(BirchfieldandTomasi

[27])Reference

(R)BThelps

at

depth

and

color

discontinuitiesTarget

(T)Stefano

Mattoccia□RRC(x,y,d)likelihood/confidenceofeachcorrespondenceEachelementC(x,y,d)oftheDSI

represents

the

cost

of

thecorrespondencebetweenIR

(xR

,y)

and

IT

(xR+d,y)TheDisparitySpaceImage

(DSI)

isa

3Dmatrix

(WxHx(dmax-

dmin)Stefano

Mattoccia•SumofAbsolutedifferences

(SAD)

•Sumof

Squareddifferences

(SSD)

•Sumoftruncated

absolute

differences

(STAD)

SArea-based

matching

costs:Stefano

Mattoccia•NormalizedCrossCorrelation

[57]•ZeromeanNormalizedCrossCorrelation

[58]•GradientbasedMF

[59]•Nonparametric

[60,61]•MutualInformation

[30]•

.

.

.•CombinationofmatchingcostsStefano

MattocciaAdd

content

hereArea-based

matching

costsStefano

MattocciaCostaggregation

(2)Let’sstartbyexaminingthe

simplestFixedWindow

(FW)

costaggregationstrategy

(TAD,disparityselectionWTA)Stefano

MattocciaWhat’swrongwithFW

?FixedWindow

(FW)Reference

(R)Target

(T)Groundtrutha)implicitlyassumesfrontal-parallel

surfacesb)ignoresdepthdiscontinuitiesc)doesnotdealexplicitlywithuniformareasd)doesnotdealexplicitlywithrepetitivepatternsc)d)b)a)FW

(withWTAreasoning)failsinmostpoints

for

the

followingreasons:Reference

(R)Target

(T)Stefano

Mattocciastrategiesrelyontheassumption

that

all

thepointsbelongingtothesupport

share

the

same

disparity

(only

fewexceptions).Stefano

MattocciaSx

Idealz

front

viewz

front

viewx

FWx

IdealSfront

viewzSz

front

viewx

FWSNevertheless,almostallstate-of-the-artcostaggregationOftenviolatedinpractice:topfigure,slanted

surfaces

(down),

etc.a)FWimplicitlyassumesfrontal-parallel

surfacesFWAggregating

the

matching

costs

of

two

populations

atdifferentdepth

(alignedforegroundandmisalignedbackground

(outliers))resultsinthe

typical

inaccuratelocalization

of

depth

borders.b)FWignoresdepthdiscontinuitiesImplicitlyassumingfrontal-parallelsurfacein

the

realscene

is

violated

near

depth

discontinuities.FWRobustmatchingmeasures

(TAD)canpartiallyreducetheSinfluenceofoutliers

Stefano

MattocciaBackgroundismisaligned

!FW

IdealForwhatconcernsFW:decreasingthe

size

of

the

supporthelps

in

reducing

the

border

localization

problem.However,thischoicerendersthe

correspondenceproblemmore

ambiguous

(especiallywhendealingwithuniformregionsandrepetitive

patterns,see

the

next

slide).Inpractice,fortheFWapproach

the

choice

of

the

optimalsizeofthe

supportis

done

empirically.State-of-the-artcostaggregationstrategiesaimat

shaping

thesupportinorder

to

includeonlypointswith

the

same(unknown)disparity.SSStefano

MattocciaInbothcasesanidealcost

aggregation

strategy

should

extenditssupportinorder

to

include

asmuchpoints

atthesame

(unknown)depthaspossible.FWdoesnotdealexplicitlywithambiguousregions

-uniform

areas

c)and

repetitive

patterns

d)SSSSFWFWStefano

MattocciaIdealIdealQuitesurprisingly,inspiteof

its

limitations,

FW

iswidely

adoptedinpractice

(probablyitisthemost

frequently

usedalgorithm

for

real

applications).•Easy/fastimplementation•Fast,thanksto

incremental

calculation

schemes•Runsinreal-timeon

standardprocessors

(SIMD)•Haslimitedmemoryrequirements•Hardwareimplementations

(FPGA)run

inreal-time

withlimitedpowerconsumption

(<1W)Beforeanalyzingmoresophisticatedapproacheslet’sconsidertwooptimizationtechniquesusedbyFWandotheralgorithms:•

Integral

Images

(II)•Box-Filtering

(BF)Stefano

MattocciaOptimization:Integral

Images(akaSummmedAreaTable)xH

I(x,y)yW•Straightforwardextensionto

stereo

(2

images)S(x,

y)

=

Σ

I(i,

j)S2

(x,

y)

=

Σ

I2

(i,

j)F.Crow,Summed-areatablesfortexturemapping,Computer

Graphics,

18(3):207–212,

1984Stefano

Mattocciai

<x

,j

<yi

<x

,j

<y

SAD(x,

y+1,

d)=

SAD(x,

y,

d)

+U(x,

y+1,

d)

M.

Mc

Donnel.

Box-filteringtechniques.ComputerGraphicsand

Image

Processing,

17:65–70,

1981Optimization:Box-Filtering1/2Left

2n+12n+1

byy+1yy+1Stefano

Mattoccia

bRightx+d2n+12n+1xOptimization:Box-Filtering2/2SAD(x,

y+1,

d)=

SAD(x,

y,

d)

+U(x,

y+1,

d)

d∈[0..dmax]

A'Dx+d-n-12n+1x+dy-nyY+1y+n+1C.x+d+n2n+1Bx-n-1

x

x+nU(x,

y+1,

d)=

U(x

_1,

y+1,

d)

+

A

_

A'_SAD(x,

y+1,

d)=

SAD(x,

y,

d)

+U(x

_1,

y+1,

d)B+Lefty-nyY+1y+n+1+A

_

A'_B

_

B'A

D2n+1

bC

_

C'_D

_

D'C

_

C'

_B

_

B'Stefano

MattocciaRightD

_

D'C2n+1+..Box-Filtering

Vs

Integral

Images-Bothrequire4

operationsperpoint-Integralimagescan

handle

supports

of

different

size-Integral

Imageshasoverflow

issues(forexample,withint32and

S2

→WxH<256x256)-Integralimages

ismoredemanding

in

terms

ofmemoryrequirements.Forsingle

images:WxHxsizeof(data_type)Vs≈Wxsizeof(int32)forS2Inpractice,integralimagesmaybeconvenientwhensupports

of

different

size

are

required.Extensionofbox-filteringtomorecomplexshapeswasproposedin

[47].Stefano

Mattoccia•Severalcomputervisionalgorithmsare

suited

for

SIMD•SIMDfeaturesareavailable

inmost

currentprocessors•IntelprocessorsSIMDavailable

since

Pentium

(MMX)•SIMDmappingis

difficult

(assembly)SIMD16

Ops

in

parallel

!OpBAOptimizations:SingleInstructionMultipleData

(SIMD)It’sacomputationparadigmthatthat

allow

forprocessingwith

the

same

operation

multiple

data

in

parallel.

a

Op

b

Scalar

computation

1

OpStefano

MattocciaSingleMatchingPhaseAlgorithm

[48,49]•

Image

type:grayscale•Preprocessing:

subtractionofmeanvalues•Matchingcost

(Step1):AbsoluteDifferences•Aggregationstrategy

(Step2):

FW•Disparityselection

(Step

3):

WTA•Outlierdetection:efficient

strategy

(later,

Step

4)•Discardsuniformareas:yes,

analyzingimage

variance•Optimizations:box-filtering+SIMD

instructions

(SSE)•Sub-pixelinterpolation

up

to

1/16of

pixel

(later)•Runsin

real-time

on

a

standardPCL.

DiStefano,

M.

Marchionni,S.

Mattoccia,

A

fastarea-basedstereomatchingalgorithmImageandVisionComputing,22(12),pp983-1005,

October2004L.

DiStefano,

M.

Marchionni,S.

Mattoccia,

APC-basedreal-timestereovisionsystemMachineGraphics&Vision,

13(3),pp.

197-220,January2004Stefano

MattocciaHowfarcanwe

gowithmoreeffective(frontalparallel)costaggregationstrategies

?Wemadeanexperimentcomputingidealfrontalparallelsupports

using

the

ground

truth.With43x43maxsupport,TAD

andaWTA

strategy:!Thereisroom

for

improvements...Results

(errorsin

red)Stefano

Mattoccia•

Comparedtopixel-basedapproachesthesupport

aggregation

(potentially)allowsforimproving

robustness•

Anideal

(frontalparallel)costaggregationstrategiesshouldincludeinthe

supportonlypointswith

similar

disparity:•

expandinginregionsat

similar

depth

(left)•

shrinkingneardepthdiscontinuities

(right)Stefano

Mattoccia•

(Unknown)Occlusionsanddiscontinuitiesplayacentralroleforsupportaggregation

strategies.The

next

slidesdepictrelevantcasesusinga

simpleobject

laying

on

aplanar

background•Occlusionsanddiscontinuitiesarestrictly

relatedWhataboutsymmetric/asymmetricsupport,discontinuitiesandocclusions

?Stefano

MattocciaCase1:nohalfocclusion,no

discontinuityStefano

MattocciaCase2:nearhalfocclusionvs

inside

discontinuityStefano

MattocciaCase3:insidehalfocclusionvs

any

->depth

=

occlusion

!!?Stefano

MattocciaCase4:nearhalfocclusionvsneardiscontinuityStefano

MattocciaCase5:nohalfocclusion,no

discontinuityStefano

MattocciaCase6:neardiscontinuity,near

occlusionStefano

MattocciaCase7:insidediscontinuity,near

occlusionStefano

MattocciaCase8:neardiscontinuity,noocclusionnodiscontinuityStefano

MattocciaCase9:insideocclusion

vs

any

->

depth

=

occlusion

!!?Stefano

MattocciaCase10:nearocclusionanddiscontinuityvsneardiscontinuityStefano

MattocciaCase11:neardiscontinuityvsneardiscontinuityStefano

MattocciaCase12:neardiscontinuityvsneardiscontinuityandocclusionStefano

MattocciaClassificationandevaluationofcostaggregationstrategiesforstereo

correspondence•In

[1]weclassified,implemented

and

evaluated

(accuracyandexecutiontime)10+state-of-the-art

cost

aggregation

strategies•Sincethefocus

is

on

the

cost

aggregation

strategy

theevaluationmethodologyincludesonlyDISCandNON_OCCF.Tombari,S.

Mattoccia,

L.

DiStefano,

E.

Addimanda,Classificationandevaluationofcost

aggregation

methods

for

stereocorrespondence,

IEEE

InternationalConferenceonComputerVisionand

Pattern

Recognition(CVPR2008)Accompanyingwebsiteandsoftware:www.vision.deis.unibo.it/spe/SPEHome.asp

Stefano

Mattoccia•Analyzedasubsetofrelevant

state-of-the-art

costaggregationstrategies•position•

shape•positionand

shape•weights•Mostofthesetechniques

compute

the

supportusing

a

symmetricstrategy•Benchmarkingplatform:IntelCoreDuo2.14

GHz

CPU•Executiontime:Teddystereopair

(size

450x373)with

andadisparitysearch

range

of

60.•Optimizations:thesameproposedbyauthors*,no

SIMD,nomulticores,etc•Thenextslidesdescribemostof

thesemethods

and

somenovelapproachesnotincludedin

thepaper

(i.e.

Fast

Aggregation

[64],FastBilateralStereo

(FBS)

[65]andtheLocallyConsistent

(LC)methodology

[66])Stefano

MattocciaFixedWindow:resultsStefano

MattocciaShiftableWindows

[11]•Thisapproachaimsatreducing

theborder

localizationproblemofFWnotconstrainingthe

support

tobe

centered

onthe

centralposition•Support

is

symm