seminario ruggero pintus, 4-10-2012
DESCRIPTION
I moderni sistemi di acquisizione 3D sono capaci di digitalizzare rapidamente la geometria e il colore di oggetti con alta accuratezza e risoluzione, producendo modelli 3D digitali con miliardi di punti. Questi modelli sono estremamente adatti nel campo dei Beni Culturali, dove è richiesto un alto livello di campionamento. Questo seminario si concentrerà su due importanti tecniche: un metodo semplice, veloce e robusto per l'allineamento semi-automatico di geometria e colore capace di gestire grandi insiemi di immagini, e un framework di blending di immagini su geometria capace di produrre modelli colorati di grandi dimensioni. L'efficacia di queste tecniche verrà dimostrata su una serie di dati reali nel campo dei Beni Culturali.TRANSCRIPT
www.crs4.it/vic/
TecnologieTecnologie didi Visual ComputingVisual Computingper per ii BeniBeni CulturaliCulturaliper per ii BeniBeni CulturaliCulturali
R. PintusCRS4 Visual Computing
R. Pintus – CRS4/ViC, October 2012
TecnologieTecnologie per per ii benibeni culturaliculturali
• Focus: digitalizzazione accurata (forma e colore) di siti e manufatti + …– Partire dai dati: Acquisizione -> Trattamento !
– Modelli misurabili
• Molti usi oltre la visualizzazione• Molti usi oltre la visualizzazione
– Riproduzione materica
– Studio di opere d’arte
– Documentazione in-situ di scavi archeologici
– Supporto al restauro e sua documentazione
– Valorizzazione
R. Pintus – CRS4/ViC, October 2012
TecnologieTecnologie per per ii benibeni culturaliculturali
• Le quantità di dati prodotte dai moderni sensori sono però difficili da trattare, archiviare, distribuire, visualizzare– Scalabilità!
• Tecniche attuali sub-ottimali• Tecniche attuali sub-ottimali– Costi, tempi, qualità
• Bisogno di ricerca in tecnologie abilitanti scalabili– Acquisizione
– Processamento geometrico
– Visualizzazione
– …
R. Pintus – CRS4/ViC, October 2012
Tecnologie per i beni culturaliTecnologie per i beni culturali
• Come acquisire e processare efficacemente forma e colore di siti e manufatti?siti e manufatti?– Tecniche di fusione multi-sensore, stream-processing, multiresolution, external
memory algorithms, parallel programming, GPGPUs
• Come archiviare e distribuire efficacemente i modelli?– Multiresolution, adaptive streaming, compression
• Come visualizzarli efficacemente?– Multiresolution, adaptive rendering, out-of-core methods, GPU programming,
parallelization, rasterization, ray-casting
• Come esplorarli?– Novel 3D displays, specific interaction techniques
– Portable devices
R. Pintus – CRS4/ViC, October 2012
AlcuniAlcuni esempiesempi
• Allineamento geometria/colore
• Colorazione di modelli 3D
• Fusione di dati e ricostruzione geometrica
• Visualizzazione scalabile ed interattiva
• Distribuzione di dati in rete• Distribuzione di dati in rete
• Esplorazione su display innovativi
(… e molto altro)
R. Pintus – CRS4/ViC, October 2012
Our GoalOur Goal
6
R. Pintus – CRS4/ViC, October 2012
Modelling vs AcquisitionModelling vs Acquisition
ModellingSubjective Reality
7
AcquisitionObjective reality
R. Pintus – CRS4/ViC, October 2012
3D Reconstruction3D Reconstruction
• Acquire geometry and color• A lot of techniques
– Structured light, laser scanning (triangulation or time-of-flight), photometric stereo, shape-from-X, …
• Which technique?
8
• Which technique?– Object type (big/small, material….) – Cost– Accuracy/Resolution– Time– Complexity
R. Pintus – CRS4/ViC, October 2012
OutlineOutline
• 3D Reconstruction Techniques
• 3D Reconstruction Pipeline – Photo mapping/blending
– Printing
9
– Printing
• Case study
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
10
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
11
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
12
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– StereoStereo
13
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– StereoStereo
14
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
15
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– MultiviewMultiview
16
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– MultiviewMultiview
17
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
18
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– SilhouettesSilhouettes
19
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
20
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– Depth from Depth from focus/defocusfocus/defocus
21
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
22
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– TransmissiveTransmissive
Computed Tomography
23
Density Function
Trasmissive Ultrasound
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
24
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– NonNon--OpticalOptical
25
Ultrasound Radar MRI
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
26
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– TimeTime--ofof--FlightFlight
27
nssm
m
c
dt 17
103
0.528 ≈
×==
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
28
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– Laser StripingLaser Striping
29
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
30
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– Structured LightingStructured Lighting
31
R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
32
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– Photometric StereoPhotometric Stereo
33
R. Pintus – CRS4/ViC, October 2012
Photometric Stereo Photometric Stereo –– SEMSEM
34
R. Pintus – CRS4/ViC, October 2012
Taxonomy Taxonomy –– Photometric StereoPhotometric Stereo
35
R. Pintus – CRS4/ViC, October 2012
Taxonomy SEMTaxonomy SEM
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
36
– Passive• Shape-from-X
– Stereo
– Multiview
– Silhouettes
– Focus/Defocus
– Active• Transmissive
– Computed Tomography (CT)
– Transmissive Ultrasound
• Reflective– Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
R. Pintus – CRS4/ViC, October 2012
Cultural HeritageCultural Heritage
• Techniques– Triangulation (laser scanner)
– Time of Flight
– Texture Mapping
– Multi-view reconstruction
– Photometric Stereo
37
– Photometric Stereo
• Deal with multiple acquisitions
• Manage a huge amount of data for visualization purposes
R. Pintus – CRS4/ViC, October 2012
3D Reconstruction Pipeline3D Reconstruction PipelineReal Object Acquisition Devices
Photos
38
3D Digital Model
=== Processing ===-Cleaning- Merging
- Photo Alignment- Color Projection
- …
Geometry
R. Pintus – CRS4/ViC, October 2012
3D Reconstruction Pipeline3D Reconstruction Pipeline
• Real Model Inspection (onsite)
• Scans design (offsite/onsite)
• Acquisition (onsite)
• Alignment (offsite)
39
• Editing (offsite)
• Merge (offsite)
• Texture (offsite)
• Final Model (offsite)
• 3D Printing (offsite)
R. Pintus – CRS4/ViC, October 2012
3D Reconstruction Pipeline3D Reconstruction Pipeline
• Real Model Inspection (onsite)
• Scans design (offsite/onsite)
• Acquisition (onsite)
• Alignment (offsite)
40
• Editing (offsite)
• Merge (offsite)
• Texture (offsite)
• Final Model (offsite)
• 3D Printing (offsite)
R. Pintus – CRS4/ViC, October 2012
GoalGoal
• Fast and low-cost technique for creating accurate colored models
• Acquisition – 3D – laser scanners
– Color – digital cameras– Color – digital cameras
• Mapping photo-to-geometry– Fast and Robust Semi-Automatic Registration of Photographs
to 3D Geometry
• Photo blending– A Streaming Framework for Seamless Detailed Photo
Blending on Massive Point Clouds
www.crs4.it/vic/
Photo MappingPhoto MappingPhoto MappingPhoto Mapping
Ruggero Pintus, Enrico Gobbetti, and Roberto Combet. “Fast and Robust Semi-Automatic Registration of Photographs to 3D Geometry”. In The 12th International Symposium on Virtual Reality, Archaeology and Cultural Heritage, October 2011.
R. Pintus – CRS4/ViC, October 2012
Problem StatementProblem Statement
3D Geometry Unordered SetOf n Uncalibrated
Photos
n Camera Poses(2D/3D Registration)
R. Pintus – CRS4/ViC, October 2012
Related workRelated work
• Manual selection of 2D-3D matches– Massive user intervention – Tiring and time-consuming
• Automatic feature matching– Not robust enough for a generic dataset
• Semi-automatic statistical correlation• Semi-automatic statistical correlation– Point cloud attributes not always provided
• Geometric multi-view reconstruction– 2D-3D problem � 3D-3D registration task
– dense and ordered frame sequence
• Our contribution– Minimize user intervention / Large datasets / Semi-
automatic / Multi-view based approach / No Attributes
R. Pintus – CRS4/ViC, October 2012
Input DataInput Data
• Dense Geometry– Point cloud, triangle
mesh, etc.
– No attributes
– No particular features
User
SfM Reconstruction
Dense 3D n Photos
– No particular features
• n photos– Naïve constraints:
• Blur, Noise, Under- or over-exposured
– Sufficient overlap
Coarse Registration
Refinement
Output Data
R. Pintus – CRS4/ViC, October 2012
MultiMulti--viewview
• Bundler [Snavely et al. 2006]– SfM system for unordered
image collections
– http://phototour.cs.washington.edu/bundler/
User
SfM Reconstruction
Dense 3D n Photos
n.edu/bundler/
• Output– A sparse point cloud
– n camera poses
– SIFT keypoints (projections of sparse 3D points)
Coarse Registration
Refinement
Output Data
R. Pintus – CRS4/ViC, October 2012
Coarse registrationCoarse registration
• Register two point clouds with different:
– scales
– reference frames
– resolutions
• Automatic methods are not
User
SfM Reconstruction
Dense 3D n Photos
• Automatic methods are not robust and efficient enough
• User aligns few images (one or more) to the dense geometry
• Affine transformation is applied to all cameras and sparse points
Coarse Registration
Refinement
Output Data
R. Pintus – CRS4/ViC, October 2012
RefinementRefinement
User
SfM Reconstruction
Dense 3D n Photos1C
jpjs ,1
js ,2
( )jpCQ ,2
jP
( )jF pNN
Coarse Registration
Refinement
Output Data
2C
js ,2
( )( )jF pNNCQ ,2
( ) ( )( )∑∑= =
−=P CN
j
N
ijijFiij spNNCQvPCE
1 1
2
,,,
R. Pintus – CRS4/ViC, October 2012
RefinementRefinement
• Sparse Bundle Adjustment (SBA)– Constants – SIFT keypoints,
dense 3D points
– Variables – Camera poses, sparse 3D points
User
SfM Reconstruction
Dense 3D n Photos
sparse 3D points
– SBA
• A Generic SBA C/C++ Package Based on the Levenberg-Marquardt Algorithm
• http://www.ics.forth.gr/~lourakis/sba/
Coarse Registration
Refinement
Output Data
R. Pintus – CRS4/ViC, October 2012
Output dataOutput data
• n camera poses
• Input of photo blending
User
SfM Reconstruction
Dense 3D n Photos
blending– n photos
– n camera poses
– Dense 3D geometry
Coarse Registration
Refinement
Output Data
R. Pintus – CRS4/ViC, October 2012
Results Results –– Photo mappingPhoto mapping
www.crs4.it/vic/
Photo BlendingPhoto BlendingPhoto BlendingPhoto Blending
Ruggero Pintus, Enrico Gobbetti, and Marco Callieri. A Streaming Framework for Seamless Detailed Photo Blending on Massive Point Clouds. In Proc. Eurographics Area Papers. Pages 25- 32, 2011.
R. Pintus – CRS4/ViC, October 2012
Problem StatementProblem Statement
Point Cloud CalibratedPhotos
R. Pintus – CRS4/ViC, October 2012
Problem StatementProblem Statement
Point Cloud CalibratedPhotos
P
R. Pintus – CRS4/ViC, October 2012
Problem StatementProblem Statement
Point Cloud CalibratedPhotos
P
ColoredPoint Cloud
R. Pintus – CRS4/ViC, October 2012
Problem StatementProblem Statement
Point Cloud CalibratedPhotos
P
ColoredPoint Cloud
• Problem ���� Unlimited size of 3D model (Gpoints) and unlimited number of images
R. Pintus – CRS4/ViC, October 2012
Related workRelated work
• State-of-the-art techniques
– Image quality estimation
– Stitching or blending
• Data representation
– Triangle meshes – exploit connectivity
– Meshless approaches– Meshless approaches
• Both triangle meshes and point clouds
• Memory settings
– All in-core – no massive geometry/images
– 3D in-core and images out-of-core – no massive geometry
– All out-of-core – Low performances
• Our contribution– Blending function / Streaming framework / Massive point cloud /
Adaptive geometry refinement
R. Pintus – CRS4/ViC, October 2012
PipelinePipeline
Photo StencilPer-pixelWeight
MaskedPer-pixelWeight
R. Pintus – CRS4/ViC, October 2012
Simple blendingSimple blending
R. Pintus – CRS4/ViC, October 2012
Edge extraction and Distance Edge extraction and Distance TransformTransform
R. Pintus – CRS4/ViC, October 2012
Smooth weightSmooth weight
R. Pintus – CRS4/ViC, October 2012
Smooth weightSmooth weight
R. Pintus – CRS4/ViC, October 2012
Single band blendingSingle band blending
R. Pintus – CRS4/ViC, October 2012
Multi band blendingMulti band blending
R. Pintus – CRS4/ViC, October 2012
Adaptive point refinementAdaptive point refinement
R. Pintus – CRS4/ViC, October 2012
Adaptive point refinementAdaptive point refinement
R. Pintus – CRS4/ViC, October 2012
Adaptive point refinementAdaptive point refinement
R. Pintus – CRS4/ViC, October 2012
Adaptive point refinementAdaptive point refinement
R. Pintus – CRS4/ViC, October 2012
ResultsResults
• Callieri et. al 2008 – David 28M
– Disk space occupancy –6.2GB
– Computation time – 15.5 hours
David470Mpoints
– Computation time – 15.5 hours
R. Pintus – CRS4/ViC, October 2012
Results Results –– Church’s ApseChurch’s Apse
14 Mpoint Geometry 40 photos
R. Pintus – CRS4/ViC, October 2012
Results Results –– Church’s ApseChurch’s Apse
R. Pintus – CRS4/ViC, October 2012
Results Results –– Grave Grave
8 Mpoint Geometry21 photos
R. Pintus – CRS4/ViC, October 2012
Results Results –– Grave Grave
R. Pintus – CRS4/ViC, October 2012
ResultsResults
R. Pintus – CRS4/ViC, October 2012
ResultsResults
R. Pintus – CRS4/ViC, October 2012
ResultsResults
David470Mpoints470Mpoints
Image size – 19456x532481Gpixel
R. Pintus – CRS4/ViC, October 2012
ResultsResults
R. Pintus – CRS4/ViC, October 2012
ConclusionConclusion
• Image-to-geometry registration approach
• Minimum user intervention
• No constraints on geometry, attributes and features
• Specific robust cost function and SBA
• Out-of-core photo blending approach (Point clouds of unlimited size)
• Incremental color accumulation (Unlimited number of images)• Incremental color accumulation (Unlimited number of images)
• Smooth weight function (Seamless color blending)
• Streaming framework (Performance improvement)
• Adaptive point refinement
• Future work
– Automatic sparse-to-dense geometry registration
– Interactive blending - adding and removing images in an interactive tool
– Fast visual check of previous alignment step
R. Pintus – CRS4/ViC, October 2012
ConclusionConclusion
• Low cost
– Personal computer
– Digital camera
– Decreased manual intervention
• Open Source / Free Software
– Bundler – SfM reconstruction –http://phototour.cs.washington.edu/bundler/http://phototour.cs.washington.edu/bundler/
– Sparse Bundle Adjustment – SBA – Minimization –http://www.ics.forth.gr/~lourakis/sba/
– Opengl / GLSL shaders – Rendering – http://www.opengl.org/
– Qt – Interface – http://qt.nokia.com/
– Opencv – Manual registration – http://opencv.willowgarage.com/wiki/
– Spaceland Library – Geometric computation –http://spacelib.sourceforge.net/
– IIPImage – Web-based Viewer – http://iipimage.sourceforge.net/
R. Pintus – CRS4/ViC, October 2012
3D Printing3D Printing
80
R. Pintus – CRS4/ViC, October 2012
Printing ProcessPrinting Process
• Original model
• Slice representation
81
representation
• Layer by layer deposition
• Cleaning
• Printed model
R. Pintus – CRS4/ViC, October 2012
Printing ProcessPrinting Process
• Original model
• Slice representation
82
representation
• Layer by layer deposition
• Cleaning
• Printed model
R. Pintus – CRS4/ViC, October 2012
Printing ProcessPrinting Process
• Original model
• Slice representation
83
representation
• Layer by layer deposition
• Cleaning
• Printed model
R. Pintus – CRS4/ViC, October 2012
Printing ProcessPrinting Process
• Original model
• Slice representation
84
representation
• Layer by layer deposition
• Cleaning
• Printed model
R. Pintus – CRS4/ViC, October 2012
Printing ProcessPrinting Process
• Original model
• Slice representation
85
representation
• Layer by layer deposition
• Cleaning
• Printed model
R. Pintus – CRS4/ViC, October 2012
Geometry processingGeometry processing
86
R. Pintus – CRS4/ViC, October 2012
Geometry processingGeometry processing
87
R. Pintus – CRS4/ViC, October 2012
Geometry processingGeometry processing
88
R. Pintus – CRS4/ViC, October 2012
SubSub--surface scatteringsurface scattering
89
R. Pintus – CRS4/ViC, October 2012
Color Color enhancementenhancement
90
R. Pintus – CRS4/ViC, October 2012
Color Color enhancementenhancement
91
R. Pintus – CRS4/ViC, October 2012
Color Color enhancementenhancement
92
R. Pintus – CRS4/ViC, October 2012
ConclusioniConclusioni
• Lavorare su dati misurati è un pre-requisito di molti lavori (tutti?) nel contesto dei beni culturali– Applicazioni specialistiche o per grande pubblico
• Le moderne tecnologie di acquisizione • Le moderne tecnologie di acquisizione consentono di acquisire una grande quantità di informazioni (forma e colore)– Laser scanning, camere digitali, ecc.
• Uso potenziale vasto!– Valorizzazione, restauro, studio, ecc.
R. Pintus – CRS4/ViC, October 2012
ConclusioniConclusioni
• Queste quantità di dati sono però difficili da trattare, archiviare, distribuire, visualizzare– Scalabilità!
• Tecniche attuali sub-ottimali• Tecniche attuali sub-ottimali– Costi, tempi, qualità
R. Pintus – CRS4/ViC, October 2012
ConclusioniConclusioni
• Il CRS4 è impegnato in attività di ricerca per migliorare le tecnologie…– Stato dell’arte internazionale
– Collaborazioni e ricadute locali
• PMI, Contro Restauro SS, Soprintendenze, CNR, UniCA• PMI, Contro Restauro SS, Soprintendenze, CNR, UniCA
• … e per applicarle a casi concreti– Collaborazioni multidisciplinari!
R. Pintus – CRS4/ViC, October 2012
ConclusioniConclusioni
96
R. Pintus – CRS4/ViC, October 2012
Questions & ContactsQuestions & Contacts
• CRS4 – VIC www.crs4.it/vic/
• Ruggero Pintus • Ruggero Pintus [email protected]