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1、DTAM:DenseTrackingandMappinginReal-TimeRichardA.Newcombe,StevenJ.LovegroveandAndrewJ.DavisonDepartmentofComputing,ImperialCollegeLondon,UK[rnewcomb,sl203,ajd]@doc.ic.ac.uktemsworkingwithahand-heldcamerahaverecentlyap-Abstractpeared(e.g.[9,13]),butthesestillrelyonfeaturetracking
2、.Herewepresentanewalgorithm,DTAM(DenseTrackingDTAMisasystemforreal-timecameratrackingandrecon-andMapping),whichunlikeallpreviousreal-timemonoc-structionwhichreliesnotonfeatureextractionbutdense,ularSLAMsystemsbothcreatesadense3Dsurfacemodeleverypixelmethods.Asasinglehand-heldRG
3、Bcameraandimmediatelyusesitfordensecameratrackingviawholefliesoverastaticscene,weestimatedetailedtextureddepthimageregistration.Asahand-heldcamerabrowsesascenemapsatselectedkeyframestoproduceasurfacepatchworkinteractively,atexture-mappedscenemodelwithmillionswithmillionsofvertic
4、es.Weusethehundredsofimagesofverticesisgenerated.Thismodeliscomposedofdepthavailableinavideostreamtoimprovethequalityofasim-mapsbuiltfrombundlesofframesbydenseandsub-pixelplephotometricdataterm,andminimiseaglobalspatiallyaccuratemulti-viewstereoreconstruction.Thereconstruc-regu
5、larisedenergyfunctionalinanovelnon-convexopti-tionframeworkistargetedatourlivesetting,wherehun-misationframework.Interleaved,wetrackthecamera’sdredsofnarrow-baselinevideoframesaretheinputtoeach6DOFmotionpreciselybyframe-ratewholeimagealign-depthmap.Wegatherphotometricinformatio
6、nsequentiallymentagainsttheentiredensemodel.Ouralgorithmsareinacostvolume,andincrementallysolveforregularisedhighlyparallelisablethroughoutandDTAMachievesreal-depthmapsviaanovelnon-convexoptimisationframeworktimeperformanceusingcurrentcommodityGPUhardware.withelementsincludinga
7、cceleratedexactexhaustivesearchWedemonstratethatadensemodelpermitssuperiortrack-toavoidcoarse-to-finewarpingandthelossofsmalldetails,ingperformanceunderrapidmotioncomparedtoastateofandaninterleavedNewtonsteptoachievefineaccuracy.theartmethodusingfeatures;andalsoshowtheadditionalM
8、eanwhile,inaninterleavedfashion,thecamera’sposeisusefu
