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
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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
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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
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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
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R. Pintus – CRS4/ViC, October 2012
Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
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– 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
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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
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Ultrasound Radar MRI
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Taxonomy (nonTaxonomy (non--destructive)destructive)
• Contact– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM
• Non-Contact
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– 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
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Edge extraction and Distance Edge extraction and Distance TransformTransform
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Smooth weightSmooth weight
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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
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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
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Printing ProcessPrinting Process
• Original model
• Slice representation
81
representation
• Layer by layer deposition
• Cleaning
• Printed model
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Printing ProcessPrinting Process
• Original model
• Slice representation
82
representation
• Layer by layer deposition
• Cleaning
• Printed model
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Printing ProcessPrinting Process
• Original model
• Slice representation
83
representation
• Layer by layer deposition
• Cleaning
• Printed model
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Printing ProcessPrinting Process
• Original model
• Slice representation
84
representation
• Layer by layer deposition
• Cleaning
• Printed model
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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
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Geometry processingGeometry processing
87
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Geometry processingGeometry processing
88
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SubSub--surface scatteringsurface scattering
89
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Color Color enhancementenhancement
90
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Color Color enhancementenhancement
91
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Color Color enhancementenhancement
92
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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
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R. Pintus – CRS4/ViC, October 2012
Questions & ContactsQuestions & Contacts
• CRS4 – VIC www.crs4.it/vic/
• Ruggero Pintus • Ruggero Pintus [email protected]
