計算機視覺中立體匹配技術的研究

計算機視覺中立體匹配技術的研究一、本文概述Overviewofthisarticle隨著科技的不斷進步，計算機視覺領域的研究日益深入，其中立體匹配技術作為實現(xiàn)三維重建和場景理解的關鍵技術，受到了廣泛的關注。立體匹配技術主要利用多視角圖像中的信息，通過匹配同名點來恢復物體的三維形狀和結(jié)構。本文旨在探討計算機視覺中立體匹配技術的研究現(xiàn)狀、發(fā)展趨勢以及面臨的挑戰(zhàn)，分析不同類型的立體匹配算法，并深入研究其中的關鍵技術和算法優(yōu)化策略。Withthecontinuousprogressoftechnology,researchinthefieldofcomputervisionisbecomingincreasinglyin-depth.Amongthem,stereomatchingtechnology,asakeytechnologyforachieving3Dreconstructionandsceneunderstanding,hasreceivedwidespreadattention.Stereomatchingtechnologymainlyutilizesinformationfrommultiviewimagestorestorethethree-dimensionalshapeandstructureofobjectsbymatchingpointswiththesamename.Thisarticleaimstoexploretheresearchstatus,developmenttrends,andchallengesofstereomatchingtechnologyincomputervision,analyzedifferenttypesofstereomatchingalgorithms,andconductin-depthresearchonkeytechnologiesandalgorithmoptimizationstrategies.本文首先介紹立體匹配技術的基本原理和流程，包括圖像獲取、預處理、特征提取與匹配、三維重建等關鍵步驟。隨后，對現(xiàn)有的立體匹配算法進行分類和總結(jié)，包括基于全局能量最小化的方法、基于局部窗口的方法、基于特征的方法等，并分析各類算法的優(yōu)缺點和適用范圍。在此基礎上，本文將重點研究基于深度學習的立體匹配算法，探討深度學習在立體匹配中的應用及其優(yōu)勢。Thisarticlefirstintroducesthebasicprinciplesandprocessesofstereomatchingtechnology,includingkeystepssuchasimageacquisition,preprocessing,featureextractionandmatching,and3Dreconstruction.Subsequently,existingstereomatchingalgorithmswereclassifiedandsummarized,includingmethodsbasedonglobalenergyminimization,methodsbasedonlocalwindows,andfeature-basedmethods.Theadvantages,disadvantages,andapplicabilityofeachtypeofalgorithmwereanalyzed.Onthisbasis,thisarticlewillfocusonresearchingstereomatchingalgorithmsbasedondeeplearning,exploringtheapplicationandadvantagesofdeeplearninginstereomatching.本文還將關注立體匹配技術在實際應用中的挑戰(zhàn)，如光照變化、遮擋、紋理缺失等問題，并分析如何通過算法優(yōu)化和技術創(chuàng)新來解決這些問題。本文將對立體匹配技術的發(fā)展趨勢進行展望，探討未來可能的研究方向和應用場景。Thisarticlewillalsofocusonthechallengesofstereomatchingtechnologyinpracticalapplications,suchaslightingchanges,occlusion,textureloss,etc.,andanalyzehowtosolvetheseproblemsthroughalgorithmoptimizationandtechnologicalinnovation.Thisarticlewillprovideanoutlookonthedevelopmenttrendofstereomatchingtechnology,andexplorepossiblefutureresearchdirectionsandapplicationscenarios.通過本文的研究，我們期望為計算機視覺領域的研究者和從業(yè)人員提供關于立體匹配技術的全面了解和深入剖析，為推動立體匹配技術的發(fā)展和應用提供有益的參考和啟示。Throughtheresearchinthisarticle,wehopetoprovideresearchersandpractitionersinthefieldofcomputervisionwithacomprehensiveunderstandingandin-depthanalysisofstereomatchingtechnology,andtoprovideusefulreferenceandinspirationforpromotingthedevelopmentandapplicationofstereomatchingtechnology.二、立體匹配技術的基本原理Thebasicprinciplesofstereomatchingtechnology立體匹配技術是計算機視覺領域中的一項重要技術，其基本原理是通過對兩幅或多幅從不同視角拍攝的圖像進行分析，尋找并匹配對應的像素點，從而恢復出場景的深度信息。立體匹配技術主要依賴于兩個基本的假設：同一物體在不同視角下的圖像中存在相似的像素點，且這些相似像素點之間存在一定的空間偏移量，即視差。Stereomatchingtechnologyisanimportanttechnologyinthefieldofcomputervision.Itsbasicprincipleistoanalyzetwoormoreimagestakenfromdifferentperspectives,findandmatchcorrespondingpixelpoints,andrestorethedepthinformationofthescene.Stereomatchingtechnologymainlyreliesontwobasicassumptions:therearesimilarpixelsofthesameobjectinimagesfromdifferentperspectives,andthereisacertainspatialoffsetbetweenthesesimilarpixels,namelyparallax.立體匹配過程通常包括預處理、特征提取、匹配和視差計算四個主要步驟。預處理階段主要是對圖像進行去噪、平滑等操作，以提高匹配精度。特征提取則是從圖像中提取出具有代表性和穩(wěn)定性的特征點或特征區(qū)域，如邊緣、角點、紋理等。在匹配階段，根據(jù)提取的特征，通過一定的匹配準則（如最近鄰、最小距離、最大互信息等）在另一幅圖像中尋找最佳匹配點。根據(jù)匹配結(jié)果和已知的相機參數(shù)，計算出每個像素點的視差值，從而得到場景的深度信息。Thestereomatchingprocessusuallyincludesfourmainsteps:preprocessing,featureextraction,matching,anddisparitycalculation.Thepreprocessingstagemainlyinvolvesdenoising,smoothing,andotheroperationsontheimagetoimprovematchingaccuracy.Featureextractionistheprocessofextractingrepresentativeandstablefeaturepointsorregionsfromanimage,suchasedges,corners,textures,etc.Inthematchingstage,basedontheextractedfeatures,thebestmatchingpointisfoundinanotherimageusingcertainmatchingcriteria(suchasnearestneighbor,minimumdistance,maximummutualinformation,etc.).Basedonthematchingresultsandknowncameraparameters,calculatethedisparityvalueofeachpixelpointtoobtainthedepthinformationofthescene.立體匹配技術的關鍵在于如何設計有效的匹配算法和選擇適當?shù)钠ヅ錅蕜t，以應對復雜多變的實際場景和噪聲干擾。近年來，隨著深度學習和卷積神經(jīng)網(wǎng)絡的發(fā)展，基于深度學習的立體匹配算法在準確性和魯棒性方面取得了顯著的進展，為計算機視覺領域的研究和應用帶來了新的突破。Thekeytostereomatchingtechnologyliesindesigningeffectivematchingalgorithmsandselectingappropriatematchingcriteriatocopewithcomplexandever-changingreal-worldscenariosandnoiseinterference.Inrecentyears,withthedevelopmentofdeeplearningandconvolutionalneuralnetworks,stereomatchingalgorithmsbasedondeeplearninghavemadesignificantprogressinaccuracyandrobustness,bringingnewbreakthroughstotheresearchandapplicationofcomputervision.三、立體匹配算法的分類與比較Classificationandcomparisonofstereomatchingalgorithms在計算機視覺領域，立體匹配技術是實現(xiàn)三維重建和場景理解的關鍵步驟。立體匹配算法的性能直接影響到三維重建的精度和效率。因此，對立體匹配算法的分類與比較具有重要的理論和實踐價值。Inthefieldofcomputervision,stereomatchingtechnologyisakeystepinachieving3Dreconstructionandsceneunderstanding.Theperformanceofstereomatchingalgorithmsdirectlyaffectstheaccuracyandefficiencyof3Dreconstruction.Therefore,theclassificationandcomparisonofstereomatchingalgorithmshaveimportanttheoreticalandpracticalvalue.根據(jù)算法的主要特點，立體匹配算法可以分為全局算法和局部算法兩大類。全局算法主要基于全局能量最小化原則，通過優(yōu)化一個包含所有數(shù)據(jù)點的全局能量函數(shù)來求解視差圖。這類算法的優(yōu)點是能夠處理復雜場景，如遮擋、紋理重復等問題，但由于需要優(yōu)化全局能量函數(shù)，計算復雜度通常較高，實時性較差。典型的全局算法有動態(tài)規(guī)劃法、圖割法、置信度傳播法等。Accordingtothemaincharacteristicsofalgorithms,stereomatchingalgorithmscanbedividedintotwocategories:globalalgorithmsandlocalalgorithms.Theglobalalgorithmismainlybasedontheprincipleofglobalenergyminimization,whichoptimizesaglobalenergyfunctioncontainingalldatapointstosolvethedisparitymap.Theadvantageofthistypeofalgorithmisthatitcanhandlecomplexscenessuchasocclusion,textureduplication,etc.However,duetotheneedtooptimizetheglobalenergyfunction,thecomputationalcomplexityisusuallyhighandthereal-timeperformanceispoor.Typicalglobalalgorithmsincludedynamicprogramming,graphcutting,confidencepropagation,andsoon.局部算法則主要基于局部窗口內(nèi)的像素信息進行匹配，通過設定一定的匹配準則（如最小絕對差、最小平方差等）來求解視差圖。這類算法的優(yōu)點是計算復雜度低，實時性好，但由于只考慮局部信息，對復雜場景的處理能力較弱。常見的局部算法有塊匹配法、特征匹配法等。Thelocalalgorithmismainlybasedonmatchingpixelinformationwithinthelocalwindow,andsolvesthedisparitymapbysettingcertainmatchingcriteria(suchasminimumabsolutedifference,minimumsquaredifference,etc.).Theadvantagesofthistypeofalgorithmarelowcomputationalcomplexityandgoodreal-timeperformance,butduetoonlyconsideringlocalinformation,itsprocessingabilityforcomplexscenesisweak.Commonlocalalgorithmsincludeblockmatching,featurematching,etc.在實際應用中，需要根據(jù)具體場景和需求選擇合適的立體匹配算法。對于需要高精度重建的場景，如機器人導航、醫(yī)療影像分析等，通常選擇全局算法以獲得更好的匹配效果；而對于需要實時處理的場景，如自動駕駛、視頻監(jiān)控等，則更傾向于選擇局部算法以保證處理速度。Inpracticalapplications,itisnecessarytochooseappropriatestereomatchingalgorithmsbasedonspecificscenariosandneeds.Forscenesthatrequirehigh-precisionreconstruction,suchasrobotnavigation,medicalimageanalysis,etc.,globalalgorithmsareusuallychosentoachievebettermatchingresults;Forscenariosthatrequirereal-timeprocessing,suchasautonomousdrivingandvideosurveillance,itismoreinclinedtochooselocalalgorithmstoensureprocessingspeed.隨著深度學習技術的發(fā)展，基于深度學習的立體匹配算法也逐漸成為研究熱點。這類算法通過訓練大量的立體圖像對來學習匹配規(guī)則，能夠?qū)崿F(xiàn)高精度的視差估計。然而，深度學習算法通常需要大量的計算資源和訓練數(shù)據(jù)，因此在實際應用中仍面臨一定的挑戰(zhàn)。Withthedevelopmentofdeeplearningtechnology,stereomatchingalgorithmsbasedondeeplearninghavegraduallybecomearesearchhotspot.Thistypeofalgorithmcanachievehigh-precisiondisparityestimationbytrainingalargenumberofstereoimagepairstolearnmatchingrules.However,deeplearningalgorithmsoftenrequirealargeamountofcomputingresourcesandtrainingdata,sotheystillfacecertainchallengesinpracticalapplications.立體匹配算法的分類與比較是一個復雜而重要的問題。不同類型的算法各有優(yōu)缺點，需要根據(jù)具體場景和需求進行選擇。未來隨著計算機視覺技術的發(fā)展，立體匹配算法也將不斷更新和完善，為三維重建和場景理解提供更加準確和高效的方法。Theclassificationandcomparisonofstereomatchingalgorithmsisacomplexandimportantissue.Differenttypesofalgorithmshavetheirownadvantagesanddisadvantages,andneedtobeselectedbasedonspecificscenariosandneeds.Withthedevelopmentofcomputervisiontechnologyinthefuture,stereomatchingalgorithmswillcontinuetobeupdatedandimproved,providingmoreaccurateandefficientmethodsfor3Dreconstructionandsceneunderstanding.四、立體匹配技術在不同場景下的應用Theapplicationofstereomatchingtechnologyindifferentscenarios立體匹配技術作為計算機視覺領域的關鍵技術之一，已經(jīng)廣泛應用于多種實際場景中。以下將詳細介紹立體匹配技術在不同領域中的應用及其效果。Stereomatchingtechnology,asoneofthekeytechnologiesinthefieldofcomputervision,hasbeenwidelyappliedinvariouspracticalscenarios.Thefollowingwillprovideadetailedintroductiontotheapplicationsandeffectsofstereomatchingtechnologyindifferentfields.在機器人導航方面，立體匹配技術為機器人提供了對環(huán)境的深度感知能力。機器人通過搭載立體相機，能夠捕捉場景中的立體信息，并通過立體匹配算法計算出物體的三維形狀和位置。這使得機器人在復雜環(huán)境中能夠自主導航、避障和完成各種任務。Intermsofrobotnavigation,stereomatchingtechnologyprovidesrobotswiththeabilitytoperceivethedepthoftheenvironment.Robotsequippedwithstereocamerascancapturestereoinformationinthesceneandcalculatethethree-dimensionalshapeandpositionofobjectsthroughstereomatchingalgorithms.Thisenablesrobotstoautonomouslynavigate,avoidobstacles,andcompletevarioustasksincomplexenvironments.在醫(yī)學影像分析領域，立體匹配技術也發(fā)揮著重要作用。醫(yī)學圖像通常包含豐富的三維結(jié)構信息，如CT、MRI等影像數(shù)據(jù)。通過立體匹配技術，醫(yī)生可以更加準確地分析病變組織的形態(tài)、位置和大小，為疾病的診斷和治療提供有力支持。Inthefieldofmedicalimageanalysis,stereomatchingtechnologyalsoplaysanimportantrole.Medicalimagestypicallycontainrichthree-dimensionalstructuralinformation,suchasCT,MRI,andotherimagingdata.Throughstereomatchingtechnology,doctorscanmoreaccuratelyanalyzetheshape,position,andsizeofdiseasedtissues,providingstrongsupportforthediagnosisandtreatmentofdiseases.自動駕駛是立體匹配技術應用的另一個重要領域。自動駕駛車輛需要實時感知周圍環(huán)境，包括道路、車輛、行人等。立體匹配技術可以幫助自動駕駛系統(tǒng)準確獲取周圍物體的三維信息，從而進行精確的路徑規(guī)劃和避障，確保行車安全。Autonomousdrivingisanotherimportantfieldfortheapplicationofstereomatchingtechnology.Autonomousvehiclesrequirereal-timeperceptionofthesurroundingenvironment,includingroads,vehicles,pedestrians,etc.Stereomatchingtechnologycanhelptheautodrivesystemaccuratelyobtainthethree-dimensionalinformationofsurroundingobjects,soastocarryoutaccuratepathplanningandobstacleavoidance,andensuredrivingsafety.虛擬現(xiàn)實和增強現(xiàn)實技術也為立體匹配技術提供了廣闊的應用空間。通過捕捉現(xiàn)實世界的立體信息，立體匹配技術可以為虛擬現(xiàn)實和增強現(xiàn)實應用提供逼真的三維場景重建。這使得用戶能夠沉浸在虛擬世界中，獲得更加真實的體驗。Virtualrealityandaugmentedrealitytechnologyalsoprovidebroadapplicationspaceforstereomatchingtechnology.Bycapturingthree-dimensionalinformationfromtherealworld,stereomatchingtechnologycanproviderealistic3Dscenereconstructionforvirtualrealityandaugmentedrealityapplications.Thisallowsuserstoimmersethemselvesinthevirtualworldandgainamorerealisticexperience.工業(yè)檢測領域同樣受益于立體匹配技術。在工業(yè)生產(chǎn)線上，立體匹配技術可以用于檢測產(chǎn)品的三維形狀和尺寸，以確保產(chǎn)品質(zhì)量。該技術還可以用于識別產(chǎn)品表面的缺陷和損傷，提高生產(chǎn)效率和質(zhì)量。Theindustrialtestingfieldalsobenefitsfromstereomatchingtechnology.Onindustrialproductionlines,stereomatchingtechnologycanbeusedtodetectthethree-dimensionalshapeandsizeofproductstoensureproductquality.Thistechnologycanalsobeusedtoidentifysurfacedefectsanddamagesofproducts,improvingproductionefficiencyandquality.立體匹配技術在不同場景下具有廣泛的應用價值。隨著技術的不斷發(fā)展和完善，相信立體匹配技術將在更多領域發(fā)揮重要作用，為人們的生產(chǎn)和生活帶來更多便利和效益。Stereomatchingtechnologyhasbroadapplicationvalueindifferentscenarios.Withthecontinuousdevelopmentandimprovementoftechnology,itisbelievedthatstereomatchingtechnologywillplayanimportantroleinmorefields,bringingmoreconvenienceandbenefitstopeople'sproductionandlife.五、立體匹配技術的優(yōu)化與改進Optimizationandimprovementofstereomatchingtechnology在計算機視覺領域，立體匹配技術是實現(xiàn)三維重建和場景理解的關鍵步驟。隨著研究的深入和應用領域的擴展，對立體匹配技術的優(yōu)化與改進顯得尤為重要。本文將從算法效率、匹配精度和魯棒性三個方面探討立體匹配技術的優(yōu)化與改進。Inthefieldofcomputervision,stereomatchingtechnologyisakeystepinachieving3Dreconstructionandsceneunderstanding.Withthedeepeningofresearchandtheexpansionofapplicationfields,theoptimizationandimprovementofstereomatchingtechnologybecomesparticularlyimportant.Thisarticlewillexploretheoptimizationandimprovementofstereomatchingtechnologyfromthreeaspects:algorithmefficiency,matchingaccuracy,androbustness.算法效率是立體匹配技術優(yōu)化與改進的首要問題。傳統(tǒng)的立體匹配算法往往面臨著計算量大、運行時間長等問題，難以滿足實時性要求較高的應用場景。因此，研究者們提出了一系列基于快速近似算法、并行計算等技術的優(yōu)化方法，旨在提高算法的運行效率。通過引入深度學習等機器學習技術，也可以實現(xiàn)算法的高效運行，同時提高匹配精度。Algorithmefficiencyistheprimaryissueforoptimizingandimprovingstereomatchingtechnology.Traditionalstereomatchingalgorithmsoftenfaceproblemssuchashighcomputationalcomplexityandlongruntime,makingitdifficulttomeethighreal-timerequirementsinapplicationscenarios.Therefore,researchershaveproposedaseriesofoptimizationmethodsbasedonfastapproximationalgorithms,parallelcomputing,andothertechnologies,aimingtoimprovetheoperationalefficiencyofthealgorithms.Byintroducingmachinelearningtechniquessuchasdeeplearning,efficientalgorithmoperationcanalsobeachievedwhileimprovingmatchingaccuracy.匹配精度是立體匹配技術優(yōu)化與改進的核心目標。為了提高匹配精度，研究者們針對傳統(tǒng)算法中的誤匹配問題，提出了多種改進策略。例如，通過引入多尺度信息、顏色空間轉(zhuǎn)換等方法，可以有效減少誤匹配現(xiàn)象?；谏疃葘W習的立體匹配算法通過訓練大量數(shù)據(jù)，可以學習到更豐富的特征信息，從而提高匹配精度。Matchingaccuracyisthecoreobjectiveofoptimizingandimprovingstereomatchingtechnology.Inordertoimprovematchingaccuracy,researchershaveproposedvariousimprovementstrategiestoaddresstheissueofmismatchesintraditionalalgorithms.Forexample,byintroducingmulti-scaleinformation,colorspaceconversion,andothermethods,thephenomenonofmismatchescanbeeffectivelyreduced.Thestereomatchingalgorithmbasedondeeplearningcanlearnricherfeatureinformationandimprovematchingaccuracybytrainingalargeamountofdata.魯棒性是立體匹配技術優(yōu)化與改進的另一個重要方面。在實際應用中，由于光照變化、噪聲干擾等因素，往往會對立體匹配結(jié)果產(chǎn)生負面影響。因此，研究者們通過引入魯棒性強的特征提取方法、優(yōu)化代價聚合策略等手段，提高算法對噪聲和光照變化的適應能力?；谏疃葘W習的立體匹配算法通過學習大量數(shù)據(jù)中的魯棒性特征，也可以提高算法的魯棒性。Robustnessisanotherimportantaspectofoptimizingandimprovingstereomatchingtechnology.Inpracticalapplications,factorssuchaslightingchangesandnoiseinterferenceoftenhaveanegativeimpactonstereomatchingresults.Therefore,researchershaveimprovedthealgorithm'sadaptabilitytonoiseandlightingchangesbyintroducingrobustfeatureextractionmethodsandoptimizingcostaggregationstrategies.Thestereomatchingalgorithmbasedondeeplearningcanalsoimprovetherobustnessofthealgorithmbylearningrobustfeaturesfromalargeamountofdata.立體匹配技術的優(yōu)化與改進是計算機視覺領域的重要研究方向。通過提高算法效率、匹配精度和魯棒性，可以更好地滿足實際應用需求，推動計算機視覺技術的發(fā)展和應用。未來，隨著深度學習等機器學習技術的進一步發(fā)展，立體匹配技術將有望實現(xiàn)更高效、更精確、更魯棒的性能，為三維重建、場景理解等任務提供更加可靠的技術支持。同時，我們也應關注到立體匹配技術在不同應用場景下的特殊需求，如無人駕駛、醫(yī)療影像分析等領域，需要針對性地優(yōu)化和改進算法，以適應不同場景下的特殊挑戰(zhàn)。Theoptimizationandimprovementofstereomatchingtechnologyisanimportantresearchdirectioninthefieldofcomputervision.Byimprovingalgorithmefficiency,matchingaccuracy,androbustness,itcanbettermeetpracticalapplicationneedsandpromotethedevelopmentandapplicationofcomputervisiontechnology.Inthefuture,withthefurtherdevelopmentofmachinelearningtechnologiessuchasdeeplearning,stereomatchingtechnologyisexpectedtoachievemoreefficient,accurate,androbustperformance,providingmorereliabletechnicalsupportfortaskssuchas3Dreconstructionandsceneunderstanding.Atthesametime,weshouldalsopayattentiontothespecialneedsofstereomatchingtechnologyindifferentapplicationscenarios,suchasautonomousdriving,medicalimageanalysis,etc.,whichrequiretargetedoptimizationandimprovementofalgorithmstoadapttothespecialchallengesindifferentscenarios.隨著大數(shù)據(jù)和云計算技術的發(fā)展，如何利用海量數(shù)據(jù)進行立體匹配算法的訓練和優(yōu)化，也是值得研究的問題。通過利用大數(shù)據(jù)和云計算資源，可以進一步提高立體匹配算法的泛化能力和魯棒性，推動計算機視覺技術在更多領域的應用和發(fā)展。Withthedevelopmentofbigdataandcloudcomputingtechnology,itisalsoworthstudyinghowtousemassivedatafortrainingandoptimizingstereomatchingalgorithms.Byutilizingbigdataandcloudcomputingresources,thegeneralizationabilityandrobustnessofstereomatchingalgorithmscanbefurtherimproved,promotingtheapplicationanddevelopmentofcomputervisiontechnologyinmorefields.立體匹配技術的優(yōu)化與改進是計算機視覺領域持續(xù)關注和研究的重要課題。通過不斷深入研究，我們有望為實際應用提供更加高效、精確和魯棒的立體匹配技術，推動計算機視覺技術的快速發(fā)展和應用。Theoptimizationandimprovementofstereomatchingtechnologyisanimportanttopicofcontinuousattentionandresearchinthefieldofcomputervision.Throughcontinuousin-depthresearch,weareexpectedtoprovidemoreefficient,accurate,androbuststereomatchingtechnologyforpracticalapplications,promotingtherapiddevelopmentandapplicationofcomputervisiontechnology.六、結(jié)論Conclusion隨著計算機視覺技術的不斷發(fā)展，立體匹配技術作為其中的關鍵部分，已經(jīng)引起了廣泛的關注和研究。本文深入探討了計算機視覺中立體匹配技術的相關研究，對現(xiàn)有的算法和方法進行了全面的分析和評價。Withthecontinuousdevelopmentofcomputervisiontechnology,stereomatchingtechnology,asakeypart,hasattractedwidespreadattentionandresearch.Thisarticledelvesintotherelevantresearchonstereomatchingtechnologyincomputervision,andprovidesacomprehensiveanalysisandevaluationofexistingalgorithmsandmethods.通過對立體匹配技術的深入研究，我們發(fā)現(xiàn)，盡管已經(jīng)有許多成熟的算法被提出并應用于實際場景，但在面對復雜多變的實際圖像時，仍然存在許多挑戰(zhàn)。例如，在紋理缺失、光照變化、噪聲干擾等情況下，立體匹配的準確性和魯棒性會受到嚴重影響