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3D Photography3D Photography
Szymon RusinkiewiczSzymon Rusinkiewicz
Fall 2002Fall 2002
3D Photography3D Photography
• Obtaining 3D shape (and sometimes Obtaining 3D shape (and sometimes color)color)of real-world objectsof real-world objects
Industrial InspectionIndustrial Inspection
• Determine whether manufactured partsDetermine whether manufactured partsare within tolerancesare within tolerances
MedicineMedicine
• Plan surgery on computer model,Plan surgery on computer model,visualize in real timevisualize in real time
MedicineMedicine
• Plan surgery on computer model,Plan surgery on computer model,visualize in real timevisualize in real time
MedicineMedicine
• Plan surgery on computer model,Plan surgery on computer model,visualize in real timevisualize in real time
MedicineMedicine
• Plan surgery on computer model,Plan surgery on computer model,visualize in real timevisualize in real time
Scanning BuildingsScanning Buildings
• Quality control during buildingQuality control during building
• As-build modelsAs-build models
Scanning BuildingsScanning Buildings
• Quality control during buildingQuality control during building
• As-build modelsAs-build models
ClothingClothing
• Scan a person, custom-fit clothingScan a person, custom-fit clothing
• U.S. Army; booths in mallsU.S. Army; booths in malls
Graphics ResearchGraphics Research
• Availability of complex datasets drives Availability of complex datasets drives researchresearch(you wouldn’t (you wouldn’t believebelieve how the poor how the poor bunny has been treated…)bunny has been treated…)
Sculpture ScanningSculpture Scanning
• The Pietà ProjectThe Pietà ProjectIBM ResearchIBM Research
• The Digital Michelangelo ProjectThe Digital Michelangelo ProjectStanford UniversityStanford University
• The Great Buddha ProjectThe Great Buddha ProjectUniversity of TokyoUniversity of Tokyo
Why Scan Sculptures?Why Scan Sculptures?
• Interesting geometryInteresting geometry
• Introduce scanning to new disciplinesIntroduce scanning to new disciplines– Art: studying working techniquesArt: studying working techniques
– Art historyArt history
– Cultural heritage preservationCultural heritage preservation
– ArcheologyArcheology
• High-visibility projectsHigh-visibility projects
Why Scan Sculptures?Why Scan Sculptures?
• ChallengingChallenging– High detail, large areasHigh detail, large areas
– Large data setsLarge data sets
– Field conditionsField conditions
– Pushing hardware, software technologyPushing hardware, software technology
• But not too challengingBut not too challenging– Simple topologySimple topology
– Possible to scan most of surfacePossible to scan most of surface
Issues AddressedIssues Addressed
• ResolutionResolution
• CoverageCoverage– Theoretical: limits of scanning technologiesTheoretical: limits of scanning technologies
– Practical: physical access, timePractical: physical access, time
• Type of dataType of data– High-res 3D data vs. coarse 3D + normal High-res 3D data vs. coarse 3D + normal
mapsmaps
– Influenced by eventual applicationInfluenced by eventual application
• Intellectual PropertyIntellectual Property
The Digital Michelangelo ProjectThe Digital Michelangelo Project
GoalsGoals
• Scan 10 sculptures by MichelangeloScan 10 sculptures by Michelangelo
• High-resolution (“quarter-millimeter”) High-resolution (“quarter-millimeter”) geometrygeometry
• Side projects: architectural scanning Side projects: architectural scanning (Accademia and Medici chapel), (Accademia and Medici chapel), scanning fragments of Forma Urbis scanning fragments of Forma Urbis RomaeRomae
Why Capture Chisel Marks?Why Capture Chisel Marks?
Atlas Atlas (Accademia)(Accademia)
ugnettougnettougnettougnetto
??
Why Capture Chisel Marks?Why Capture Chisel Marks?
Day (Medici Day (Medici Chapel)Chapel)
2 mm2 mm
Who?Who?
Faculty and staffFaculty and staffProf. Brian CurlessProf. Brian Curless John GerthJohn Gerth
Jelena JovanovicJelena Jovanovic Prof. Marc LevoyProf. Marc Levoy
Lisa PacelleLisa Pacelle Domi Pitturo Domi Pitturo
Dr. Kari PulliDr. Kari Pulli
Graduate studentsGraduate studentsSean AndersonSean Anderson Barbara CaputoBarbara Caputo
James DavisJames Davis Dave KollerDave Koller
Lucas PereiraLucas Pereira Szymon RusinkiewiczSzymon Rusinkiewicz
Jonathan ShadeJonathan Shade Marco TariniMarco Tarini
Daniel WoodDaniel Wood
UndergraduatesUndergraduatesAlana ChanAlana Chan Kathryn ChinnKathryn Chinn
Jeremy GinsbergJeremy Ginsberg Matt GinztonMatt Ginzton
Unnur GretarsdottirUnnur Gretarsdottir Rahul GuptaRahul Gupta
Wallace HuangWallace Huang Dana KatterDana Katter
Ephraim LuftEphraim Luft Dan PerkelDan Perkel
Semira RahemtullaSemira Rahemtulla Alex RoetterAlex Roetter
Joshua SchroederJoshua Schroeder Maisie TsuiMaisie Tsui
David WeeklyDavid Weekly
In FlorenceIn FlorenceDottssa Cristina Acidini Dottssa Cristina Acidini Dottssa Franca FallettiDottssa Franca Falletti
Dottssa Licia BertaniDottssa Licia Bertani Alessandra MarinoAlessandra Marino
Matti AuvinenMatti Auvinen
In RomeIn RomeProf. Eugenio La RoccaProf. Eugenio La Rocca Dottssa Susanna Le PeraDottssa Susanna Le Pera
Dottssa Anna SomellaDottssa Anna Somella Dottssa Laura FerreaDottssa Laura Ferrea
In PisaIn PisaRoberto ScopignoRoberto Scopigno
SponsorsSponsorsInterval ResearchInterval Research Paul G. Allen Foundation for the ArtsPaul G. Allen Foundation for the Arts
Stanford UniversityStanford University
Equipment donorsEquipment donorsCyberwareCyberware Cyra TechnologiesCyra Technologies
Faro TechnologiesFaro Technologies IntelIntel
Silicon GraphicsSilicon Graphics SonySony
3D Scanners3D Scanners
Scanner DesignScanner Design
4 motorized axes4 motorized axes
laser, range camera,laser, range camera,white light, and color camerawhite light, and color camera
TriangulationTriangulation
• Project laser stripe onto objectProject laser stripe onto object
ObjectObject
LaserLaser
CameraCameraCameraCamera
CameraCameraCameraCamera
TriangulationTriangulation
• Depth from ray-plane triangulationDepth from ray-plane triangulation
LaserLaser
(x,y(x,y))
ObjectObject
Scanning a Large ObjectScanning a Large Object
• Calibrated motionsCalibrated motions– pitch pitch (yellow)(yellow)
– pan pan (blue)(blue)
– horizontal translation horizontal translation (orange)(orange)
• Uncalibrated motionsUncalibrated motions– vertical translationvertical translation
– rolling the gantryrolling the gantry
– remounting the scan headremounting the scan head
Single Scan of St. MatthewSingle Scan of St. Matthew 1 mm1 mm1 mm1 mm
Statistics About the Scan of DavidStatistics About the Scan of David
• 480 individually aimed 480 individually aimed scansscans
• 0.3 mm sample spacing0.3 mm sample spacing
• 2 billion polygons2 billion polygons
• 7,000 color images7,000 color images
• 32 gigabytes32 gigabytes
• 30 nights of scanning30 nights of scanning
• 22 people22 people
Head of Michelangelo’s Head of Michelangelo’s DavidDavid
PhotographPhotograph 1.0 mm computer model1.0 mm computer model
Side project:Side project:The Forma Urbis RomaeThe Forma Urbis Romae
Forma Urbis Romae FragmentForma Urbis Romae Fragment
side face
IBM’s Pietà ProjectIBM’s Pietà Project
• Michelangelo’s Michelangelo’s “Florentine Pietà”“Florentine Pietà”
• Late work (1550s)Late work (1550s)
• Partially destroyed by Partially destroyed by Michelangelo, Michelangelo, recreated by his recreated by his studentstudent
• Currently in the Museo Currently in the Museo dell’Opera del Duomodell’Opera del Duomoin Florencein Florence
Who?Who?
• Dr. Jack Wasserman, professor emeritus Dr. Jack Wasserman, professor emeritus of art history at Temple Universityof art history at Temple University
• Visual and Geometric Computing groupVisual and Geometric Computing group@ IBM Research:@ IBM Research:
Fausto BernardiniHolly RushmeierIoana MartinJoshua Mittleman
Gabriel TaubinAndre GueziecClaudio Silva
ScannerScanner
• Visual Interface “Virtuoso”Visual Interface “Virtuoso”
• Active multibaseline stereoActive multibaseline stereo
• Projector (stripe pattern),Projector (stripe pattern),6 B&W cameras, 1 color camera6 B&W cameras, 1 color camera
• Augmented with 5 extraAugmented with 5 extra“point” light sources for“point” light sources forphotometric stereophotometric stereo(active shape from shading)(active shape from shading)
DataData
• Range data has 2 mm spacing, 0.1mm Range data has 2 mm spacing, 0.1mm noisenoise
• Each range image: 10,000 points, Each range image: 10,000 points, 202020 cm20 cm
• Color data: 5 images with controlled Color data: 5 images with controlled lighting, 1280lighting, 1280960, 0.5 mm resolution960, 0.5 mm resolution
• Total of 770 scans, 7.2 million pointsTotal of 770 scans, 7.2 million points
ScanningScanning
• Final scan June 1998, Final scan June 1998, completed July 1999completed July 1999
• Total scanning time: Total scanning time: 90 hours over 14 90 hours over 14 daysdays(includes equipment (includes equipment setup time)setup time)
ResultsResults
The Great Buddha ProjectThe Great Buddha Project
• Great Buddha of KamakuraGreat Buddha of Kamakura
• Original made of wood, completed 1243Original made of wood, completed 1243
• Covered in bronze and gold leaf, 1267Covered in bronze and gold leaf, 1267
• Approx. 15 m tallApprox. 15 m tall
• Goal: preservation ofGoal: preservation ofcultural heritagecultural heritage
Who?Who?
• Institute of Industrial Science,Institute of Industrial Science,University of TokyoUniversity of Tokyo
Daisuke MiyazakiDaisuke MiyazakiTakeshi OoishiTakeshi OoishiTaku NishikawaTaku NishikawaRyusuke SagawaRyusuke Sagawa
Ko NishinoKo NishinoTakashi TomomatsuTakashi TomomatsuYutaka TakaseYutaka TakaseKatsushi IkeuchiKatsushi Ikeuchi
ScannerScanner
• Cyrax range scanner by Cyra Cyrax range scanner by Cyra TechnologiesTechnologies
• Laser pulse time-of-flightLaser pulse time-of-flight
Pulsed Time of FlightPulsed Time of Flight
• Send out pulse of light (usually laser),Send out pulse of light (usually laser),time how long it takes to returntime how long it takes to return
tcd 2
1tcd
2
1
Pulsed Time of FlightPulsed Time of Flight
• Advantages:Advantages:– Large working volume (up to 100 m.)Large working volume (up to 100 m.)
• Disadvantages:Disadvantages:– Not-so-great accuracy (at best ~5 mm.)Not-so-great accuracy (at best ~5 mm.)
• Requires getting timing to ~30 picosecondsRequires getting timing to ~30 picoseconds
• Does not scale with working volumeDoes not scale with working volume
• Often used for scanning buildings, Often used for scanning buildings, rooms, archeological sites, etc.rooms, archeological sites, etc.
DataData
• 20 range images (a few million points)20 range images (a few million points)
• 4 mm accuracy4 mm accuracy
• Several nights of scanningSeveral nights of scanning
ResultsResults
CourseCourse
• Meets Mondays 11-12, Wednesdays 11-Meets Mondays 11-12, Wednesdays 11-12:2012:20
• Class project – build a 3D photography Class project – build a 3D photography system from the ground upsystem from the ground up
• http://www.cs.princeton.edu/courses/http://www.cs.princeton.edu/courses/597B/597B/