SlideShare una empresa de Scribd logo
1 de 10
Descargar para leer sin conexión
1
Introduction to 3D
digitization
technologies
Roberto Scopigno
Visual Computing Lab.
CNR-ISTI
Pisa, Italy
R. Scopigno, 3D Digitization - HW 1
Overview
o  Digitization for visual presentation: 3D
vs. enhanced 2D media
o  3D digitization technologies
2
2
Acquiring Visually Rich 3D Models
Goal:
Build accurate digital models to
clone the reality (shape +
surface reflection properties)
Acquisition methodologies:
n  Image-based Rendering
o  Panoramic images (2D)
o  RTI images (2D)
n  Standard CAD modeling
(manual process)
n  Approaches based on
Sampling
o  3D scanning (active)
o  3D from images (passive)
3
Modelling vs. Sampling
o  Modelling
n  Manual process
[“redraw”]
n  Accuracy is unknown
n  3D model is usually
complete
o  Sampling/scanning
n  Semi-automatic process
[“photography”]
n  Accuracy is known
n  3D model is usually
uncomplete (many
unsampled regions)
R. Scopigno, 3D Digitization - HW
3
R. Scopigno, 3D Digitization - HW 4
3D scanning devices
Many different technologies, just two
examples:
o  Laser or structured light,
Triangulation
n  Small/medium scale artifacts (statues)
n  Small/medium workspace 20x20 ->
100x100 cm, distance from artifact ~1 m
n  High accuracy (>0.05 mm)
n  High sampling density (0.2 mm)
n  Fast (1 shot in ~1-2 sec)
o  Laser, Time of flight
n  Large scale (architectures)
n  Wide workspace (many meters)
n  Medium accuracy (~4-10 mm)
n  Medium sampling density (10 mm)
n  Slow (1 shot in ~20 min)
R. Scopigno, 3D Digitization - HW 5
Active Optical Technologies
o  Using light is much faster than using
a physical probe
o  Allows also scanning of soft or fragile
objects which would be threatened by
probing
o  Three types of optical sensing:
n  Point, similar to a physical
probe
o slow approach, lots of physical
movement by the sensor.
n  Stripe
o  faster: a band of many points
passes over the object at once
n Other patterns …
4
R. Scopigno, 3D Digitization - HW 6
Stripe-based scanning
R. Scopigno, 3D Digitization - HW 7
Optical Technologies - Triangulation
How do we compute the 3D
coordinates of each sampled
point?
o  By triangulation, known:
n  emitting point of the
light source + direction
(illuminant or
emitter)
n  the focus point of the
acquisition camera
(sensor)
n  the center of the
imaged reflection on the
acquisition sensor plane
( P(a) )
Triangulation is an old, simple approach (Thales-Talete)
Issues: precision and price of the system
5
R. Scopigno, 3D Digitization - HW 8
Output: range map
R. Scopigno, 3D Digitization - HW 9
Triangulation-based systems
An inherent limitation of the
triangulation approach:
non-visible regions
o  Some surface regions can be
visible to the emitter and
not-visible to the receiver,
and vice-versa
o  In all these regions we miss
sampled points è
integration of multiple scans
6
R. Scopigno, 3D Digitization - HW 10
Scanning example
R. Scopigno, 3D Digitization - HW 11
Acquisition accuracy
o  Depends on sweeping
approach …
o  … on surface curvature
w.r.t. light direction …
o  Laser syst.: the
reflected intensity can
be used as an estimate of
the accuracy of the
measure
7
R. Scopigno, 3D Digitization - HW 12
Acquisition accuracy
o  … on the surface shape nearby the sampled point
o  … and on surface reflectance
[see Curless Levoy “…Space Time Analysis”, ’95]
R. Scopigno, 3D Digitization - HW 13
Optical Tech. – Time of Flight
Measure the time a light impulse needs to travel from the emitter to the
target point (and back)
n  Source: emits a light pulse and starts a nanosecond watch
n  Sensor: detects the reflected light, stops the watch
(roundtrip time)
n  Distance = ½ time * lightspeed [e.g. 6.67 ns è 1 m ]
o  Advantages: no triangulation, source and receiver can be on the
same axis è smaller footprint (wide distance measures), no shadow
effects
[Image by R. Lange et al, SPIE v.3823]
8
R. Scopigno, 3D Digitization - HW 14
Optical– Time of Flight
o  Optical signal:
n  Pulsed light: easier to be detected, more complex to be
generated at high frequency (short pulses, fast rise and fall
times)
n  Modulated light (sine waves, intensity): phase difference
between sent and received signal è distance (modulo
wavelenght)
n  A combination of the previous (pulsed sine)
o  Scanning:
n  single spot measure
n  range map, by rotating mirrors
or motorized 2 DOF head
[Image by Brian Curless,
Sig2000 CourseNotes]
R. Scopigno, 3D Digitization - HW 15
3D scanning – raw output data
For the user, same type of output data :
n  Range map: 2D map of sampled 3D points
(640x480 -> 2M - 5M points)
n  Can be managed as a point cloud or a
triangulated surface chunk
9
R. Scopigno, 3D Digitization - HW 16
Why processing raw scanned data?
The acquisition of a single shot
(range map) is only a single step
in the 3D scanning process, since
it returns a partial & incomplete
representation
dal parziale
al totale
We need algorithms and software
tools for transforming redundandt
sampled data into a complete and
optimal 3D model

3D Scanning Pipeline
R. Scopigno, 3D Digitization - HW 17
10
Note:
New approaches appeared that use many
redundant & overlapping images to
produce results similar to those produced
with active scanning devices
è
3D from images (passive methods)
R. Scopigno, 3D Digitization - HW 18
R. Scopigno, 3D Digitization - HW 19
Questions?
o  Contact:
Visual Computing Lab.
of ISTI - CNR
http://vcg.isti.cnr.it
r.scopigno@isti.cnr.it

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

3D Printing Technology
3D Printing Technology3D Printing Technology
3D Printing Technology
 
3 d scanning technology
3 d scanning technology3 d scanning technology
3 d scanning technology
 
SELECTIVE LASER SINTERING
SELECTIVE LASER SINTERING SELECTIVE LASER SINTERING
SELECTIVE LASER SINTERING
 
Poly jet ppt
Poly jet pptPoly jet ppt
Poly jet ppt
 
3d printing
3d printing3d printing
3d printing
 
Stereolithography latest
Stereolithography latestStereolithography latest
Stereolithography latest
 
Neutron radiography
Neutron radiographyNeutron radiography
Neutron radiography
 
3D printing
3D printing3D printing
3D printing
 
Micro molding milling
Micro molding millingMicro molding milling
Micro molding milling
 
selective laser sintering;a rapid prototyping technology
selective laser sintering;a rapid prototyping technologyselective laser sintering;a rapid prototyping technology
selective laser sintering;a rapid prototyping technology
 
Image Sensor PPT
Image Sensor PPTImage Sensor PPT
Image Sensor PPT
 
3d printing
3d printing 3d printing
3d printing
 
Additive manufacturing ppt2
Additive manufacturing ppt2Additive manufacturing ppt2
Additive manufacturing ppt2
 
CCD and CMOS Image Sensor
CCD and CMOS Image SensorCCD and CMOS Image Sensor
CCD and CMOS Image Sensor
 
Report on 3D printing , types, application, challenges
Report on 3D printing , types, application, challengesReport on 3D printing , types, application, challenges
Report on 3D printing , types, application, challenges
 
3 D printing
3 D printing3 D printing
3 D printing
 
Additive Manufacturing (3-D printing) , Rapid Prototyping
Additive Manufacturing (3-D printing) , Rapid PrototypingAdditive Manufacturing (3-D printing) , Rapid Prototyping
Additive Manufacturing (3-D printing) , Rapid Prototyping
 
Additive manufacturing and 3 d printing
Additive manufacturing and 3 d printing Additive manufacturing and 3 d printing
Additive manufacturing and 3 d printing
 
Solid ground curing
Solid ground curingSolid ground curing
Solid ground curing
 
MINI REPORT 1.pdf
MINI REPORT 1.pdfMINI REPORT 1.pdf
MINI REPORT 1.pdf
 

Similar a 3d scanning techniques

Generation and weighting of 3D point correspondences for improved registratio...
Generation and weighting of 3D point correspondences for improved registratio...Generation and weighting of 3D point correspondences for improved registratio...
Generation and weighting of 3D point correspondences for improved registratio...Kourosh Khoshelham
 
Ray casting algorithm by mhm
Ray casting algorithm by mhmRay casting algorithm by mhm
Ray casting algorithm by mhmMd Mosharof Hosen
 
Sensor modeling and Photometry: an application to Astrophotography
Sensor modeling and Photometry: an application to AstrophotographySensor modeling and Photometry: an application to Astrophotography
Sensor modeling and Photometry: an application to AstrophotographyLaurent Devineau
 
3D Image visualization
3D Image visualization3D Image visualization
3D Image visualizationalok ray
 
Depth estimation do we need to throw old things away
Depth estimation do we need to throw old things awayDepth estimation do we need to throw old things away
Depth estimation do we need to throw old things awayNAVER Engineering
 
Build Your Own 3D Scanner: 3D Scanning with Swept-Planes
Build Your Own 3D Scanner: 3D Scanning with Swept-PlanesBuild Your Own 3D Scanner: 3D Scanning with Swept-Planes
Build Your Own 3D Scanner: 3D Scanning with Swept-PlanesDouglas Lanman
 
From STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to Blender
From STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to BlenderFrom STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to Blender
From STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to BlenderEmanuele Simioni
 
A Review over Different Blur Detection Techniques in Image Processing
A Review over Different Blur Detection Techniques in Image ProcessingA Review over Different Blur Detection Techniques in Image Processing
A Review over Different Blur Detection Techniques in Image Processingpaperpublications3
 
EC4160-lect 1,2.ppt
EC4160-lect 1,2.pptEC4160-lect 1,2.ppt
EC4160-lect 1,2.pptssuser812128
 
image segmentation by ppres.pptx
image segmentation by ppres.pptximage segmentation by ppres.pptx
image segmentation by ppres.pptxmohan134666
 
DIPsadasdasfsdfsdfdfasdfsdfsdgsdgdsfgdfgfdg
DIPsadasdasfsdfsdfdfasdfsdfsdgsdgdsfgdfgfdgDIPsadasdasfsdfsdfdfasdfsdfsdgsdgdsfgdfgfdg
DIPsadasdasfsdfsdfdfasdfsdfsdgsdgdsfgdfgfdgMrVMNair
 
Focused Image Creation Algorithms for digital holography
Focused Image Creation Algorithms for digital holographyFocused Image Creation Algorithms for digital holography
Focused Image Creation Algorithms for digital holographyConor Mc Elhinney
 

Similar a 3d scanning techniques (20)

3d scanning pipeline
3d scanning pipeline3d scanning pipeline
3d scanning pipeline
 
Generation and weighting of 3D point correspondences for improved registratio...
Generation and weighting of 3D point correspondences for improved registratio...Generation and weighting of 3D point correspondences for improved registratio...
Generation and weighting of 3D point correspondences for improved registratio...
 
Ray casting algorithm by mhm
Ray casting algorithm by mhmRay casting algorithm by mhm
Ray casting algorithm by mhm
 
Sensor modeling and Photometry: an application to Astrophotography
Sensor modeling and Photometry: an application to AstrophotographySensor modeling and Photometry: an application to Astrophotography
Sensor modeling and Photometry: an application to Astrophotography
 
3D Image visualization
3D Image visualization3D Image visualization
3D Image visualization
 
Photogrammetry 1.
Photogrammetry 1.Photogrammetry 1.
Photogrammetry 1.
 
RWDA
RWDARWDA
RWDA
 
Orb feature by nitin
Orb feature by nitinOrb feature by nitin
Orb feature by nitin
 
Computer Vision
Computer VisionComputer Vision
Computer Vision
 
D04432528
D04432528D04432528
D04432528
 
Depth estimation do we need to throw old things away
Depth estimation do we need to throw old things awayDepth estimation do we need to throw old things away
Depth estimation do we need to throw old things away
 
Modern Equipment's in Survey Works
Modern Equipment's in Survey WorksModern Equipment's in Survey Works
Modern Equipment's in Survey Works
 
Build Your Own 3D Scanner: 3D Scanning with Swept-Planes
Build Your Own 3D Scanner: 3D Scanning with Swept-PlanesBuild Your Own 3D Scanner: 3D Scanning with Swept-Planes
Build Your Own 3D Scanner: 3D Scanning with Swept-Planes
 
From STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to Blender
From STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to BlenderFrom STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to Blender
From STC (Stereo Camera onboard on Bepi Colombo ESA Mission) to Blender
 
A Review over Different Blur Detection Techniques in Image Processing
A Review over Different Blur Detection Techniques in Image ProcessingA Review over Different Blur Detection Techniques in Image Processing
A Review over Different Blur Detection Techniques in Image Processing
 
EC4160-lect 1,2.ppt
EC4160-lect 1,2.pptEC4160-lect 1,2.ppt
EC4160-lect 1,2.ppt
 
image segmentation by ppres.pptx
image segmentation by ppres.pptximage segmentation by ppres.pptx
image segmentation by ppres.pptx
 
DIPsadasdasfsdfsdfdfasdfsdfsdgsdgdsfgdfgfdg
DIPsadasdasfsdfsdfdfasdfsdfsdgsdgdsfgdfgfdgDIPsadasdasfsdfsdfdfasdfsdfsdgsdgdsfgdfgfdg
DIPsadasdasfsdfsdfdfasdfsdfsdgsdgdsfgdfgfdg
 
Focused Image Creation Algorithms for digital holography
Focused Image Creation Algorithms for digital holographyFocused Image Creation Algorithms for digital holography
Focused Image Creation Algorithms for digital holography
 
final_presentation
final_presentationfinal_presentation
final_presentation
 

Más de Frederic Kaplan

Les technologies absorbantes
Les technologies absorbantesLes technologies absorbantes
Les technologies absorbantesFrederic Kaplan
 
Transformer 4 millions d'articles de presse en un système d'information
Transformer 4 millions d'articles de presse en un système d'informationTransformer 4 millions d'articles de presse en un système d'information
Transformer 4 millions d'articles de presse en un système d'informationFrederic Kaplan
 
L'historien et l'algorithme : Présentation aux Entretiens du Nouveau Monde In...
L'historien et l'algorithme : Présentation aux Entretiens du Nouveau Monde In...L'historien et l'algorithme : Présentation aux Entretiens du Nouveau Monde In...
L'historien et l'algorithme : Présentation aux Entretiens du Nouveau Monde In...Frederic Kaplan
 
DH101 2013/2014 course 10 - 3d printing, Javascript data visualization
DH101 2013/2014 course 10 - 3d printing, Javascript data visualization DH101 2013/2014 course 10 - 3d printing, Javascript data visualization
DH101 2013/2014 course 10 - 3d printing, Javascript data visualization Frederic Kaplan
 
DH101 2013/2014 course 9 - Crowdsourcing, crowdfunding, Wikipedia, Open Stree...
DH101 2013/2014 course 9 - Crowdsourcing, crowdfunding, Wikipedia, Open Stree...DH101 2013/2014 course 9 - Crowdsourcing, crowdfunding, Wikipedia, Open Stree...
DH101 2013/2014 course 9 - Crowdsourcing, crowdfunding, Wikipedia, Open Stree...Frederic Kaplan
 
DH101 2013/2014 course 8 - Historical Geographical Information Systems (HGIS)...
DH101 2013/2014 course 8 - Historical Geographical Information Systems (HGIS)...DH101 2013/2014 course 8 - Historical Geographical Information Systems (HGIS)...
DH101 2013/2014 course 8 - Historical Geographical Information Systems (HGIS)...Frederic Kaplan
 
DH101 2013/2014 Projects
DH101 2013/2014 ProjectsDH101 2013/2014 Projects
DH101 2013/2014 ProjectsFrederic Kaplan
 
DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recogni...
DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recogni...DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recogni...
DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recogni...Frederic Kaplan
 
DH101 2013/2014 course 6 - Semantic coding, RDF, CIDOC-CRM
DH101 2013/2014 course 6 - Semantic coding, RDF, CIDOC-CRMDH101 2013/2014 course 6 - Semantic coding, RDF, CIDOC-CRM
DH101 2013/2014 course 6 - Semantic coding, RDF, CIDOC-CRMFrederic Kaplan
 
DH101 2013/2014 course 5 - Project on Venice / Datafication / Regulated repre...
DH101 2013/2014 course 5 - Project on Venice / Datafication / Regulated repre...DH101 2013/2014 course 5 - Project on Venice / Datafication / Regulated repre...
DH101 2013/2014 course 5 - Project on Venice / Datafication / Regulated repre...Frederic Kaplan
 
DH101 2013/2014 course 4 - Digitization techniques 2D and 3D
DH101 2013/2014 course 4 - Digitization techniques 2D and 3D DH101 2013/2014 course 4 - Digitization techniques 2D and 3D
DH101 2013/2014 course 4 - Digitization techniques 2D and 3D Frederic Kaplan
 
3d scanning for digital heritage
3d scanning for digital heritage3d scanning for digital heritage
3d scanning for digital heritageFrederic Kaplan
 
Franziska Frey 2 / DHV13
Franziska Frey 2 / DHV13Franziska Frey 2 / DHV13
Franziska Frey 2 / DHV13Frederic Kaplan
 
Franziska Frey 1 / DHV13
Franziska Frey 1 / DHV13Franziska Frey 1 / DHV13
Franziska Frey 1 / DHV13Frederic Kaplan
 
Color and appearance information in 3d models
Color and appearance information in 3d modelsColor and appearance information in 3d models
Color and appearance information in 3d modelsFrederic Kaplan
 
Digital Humanities Venice Fall School: Introduction
Digital Humanities Venice Fall School: IntroductionDigital Humanities Venice Fall School: Introduction
Digital Humanities Venice Fall School: IntroductionFrederic Kaplan
 
DH101 2013/2014 course 3 - Panoramic intensifcation, narrative crise and intr...
DH101 2013/2014 course 3 - Panoramic intensifcation, narrative crise and intr...DH101 2013/2014 course 3 - Panoramic intensifcation, narrative crise and intr...
DH101 2013/2014 course 3 - Panoramic intensifcation, narrative crise and intr...Frederic Kaplan
 

Más de Frederic Kaplan (20)

Les technologies absorbantes
Les technologies absorbantesLes technologies absorbantes
Les technologies absorbantes
 
La langue comme capital
La langue comme capitalLa langue comme capital
La langue comme capital
 
Transformer 4 millions d'articles de presse en un système d'information
Transformer 4 millions d'articles de presse en un système d'informationTransformer 4 millions d'articles de presse en un système d'information
Transformer 4 millions d'articles de presse en un système d'information
 
L'historien et l'algorithme : Présentation aux Entretiens du Nouveau Monde In...
L'historien et l'algorithme : Présentation aux Entretiens du Nouveau Monde In...L'historien et l'algorithme : Présentation aux Entretiens du Nouveau Monde In...
L'historien et l'algorithme : Présentation aux Entretiens du Nouveau Monde In...
 
DH101 2013/2014 course 10 - 3d printing, Javascript data visualization
DH101 2013/2014 course 10 - 3d printing, Javascript data visualization DH101 2013/2014 course 10 - 3d printing, Javascript data visualization
DH101 2013/2014 course 10 - 3d printing, Javascript data visualization
 
DH101 2013/2014 course 9 - Crowdsourcing, crowdfunding, Wikipedia, Open Stree...
DH101 2013/2014 course 9 - Crowdsourcing, crowdfunding, Wikipedia, Open Stree...DH101 2013/2014 course 9 - Crowdsourcing, crowdfunding, Wikipedia, Open Stree...
DH101 2013/2014 course 9 - Crowdsourcing, crowdfunding, Wikipedia, Open Stree...
 
DH101 2013/2014 course 8 - Historical Geographical Information Systems (HGIS)...
DH101 2013/2014 course 8 - Historical Geographical Information Systems (HGIS)...DH101 2013/2014 course 8 - Historical Geographical Information Systems (HGIS)...
DH101 2013/2014 course 8 - Historical Geographical Information Systems (HGIS)...
 
DH101 2013/2014 Projects
DH101 2013/2014 ProjectsDH101 2013/2014 Projects
DH101 2013/2014 Projects
 
DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recogni...
DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recogni...DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recogni...
DH101 2013/2014 course 7 - OCR, Printed text recognition, Handwriting recogni...
 
DH101 2013/2014 course 6 - Semantic coding, RDF, CIDOC-CRM
DH101 2013/2014 course 6 - Semantic coding, RDF, CIDOC-CRMDH101 2013/2014 course 6 - Semantic coding, RDF, CIDOC-CRM
DH101 2013/2014 course 6 - Semantic coding, RDF, CIDOC-CRM
 
DH101 2013/2014 course 5 - Project on Venice / Datafication / Regulated repre...
DH101 2013/2014 course 5 - Project on Venice / Datafication / Regulated repre...DH101 2013/2014 course 5 - Project on Venice / Datafication / Regulated repre...
DH101 2013/2014 course 5 - Project on Venice / Datafication / Regulated repre...
 
DH101 2013/2014 course 4 - Digitization techniques 2D and 3D
DH101 2013/2014 course 4 - Digitization techniques 2D and 3D DH101 2013/2014 course 4 - Digitization techniques 2D and 3D
DH101 2013/2014 course 4 - Digitization techniques 2D and 3D
 
3d scanning for digital heritage
3d scanning for digital heritage3d scanning for digital heritage
3d scanning for digital heritage
 
Franziska Frey 2 / DHV13
Franziska Frey 2 / DHV13Franziska Frey 2 / DHV13
Franziska Frey 2 / DHV13
 
Franziska Frey 1 / DHV13
Franziska Frey 1 / DHV13Franziska Frey 1 / DHV13
Franziska Frey 1 / DHV13
 
Color and appearance information in 3d models
Color and appearance information in 3d modelsColor and appearance information in 3d models
Color and appearance information in 3d models
 
3d from images
3d from images3d from images
3d from images
 
Pellegrini small
Pellegrini smallPellegrini small
Pellegrini small
 
Digital Humanities Venice Fall School: Introduction
Digital Humanities Venice Fall School: IntroductionDigital Humanities Venice Fall School: Introduction
Digital Humanities Venice Fall School: Introduction
 
DH101 2013/2014 course 3 - Panoramic intensifcation, narrative crise and intr...
DH101 2013/2014 course 3 - Panoramic intensifcation, narrative crise and intr...DH101 2013/2014 course 3 - Panoramic intensifcation, narrative crise and intr...
DH101 2013/2014 course 3 - Panoramic intensifcation, narrative crise and intr...
 

Último

Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Will Schroeder
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Adtran
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxMatsuo Lab
 
Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.francesco barbera
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfAijun Zhang
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URLRuncy Oommen
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8DianaGray10
 
Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfAnna Loughnan Colquhoun
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024D Cloud Solutions
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1DianaGray10
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaborationbruanjhuli
 
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostMatt Ray
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1DianaGray10
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxYounusS2
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintMahmoud Rabie
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...Aggregage
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureEric D. Schabell
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfDaniel Santiago Silva Capera
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Websitedgelyza
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfDianaGray10
 

Último (20)

Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptx
 
Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdf
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URL
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8
 
Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdf
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
 
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptx
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership Blueprint
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability Adventure
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Website
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
 

3d scanning techniques

  • 1. 1 Introduction to 3D digitization technologies Roberto Scopigno Visual Computing Lab. CNR-ISTI Pisa, Italy R. Scopigno, 3D Digitization - HW 1 Overview o  Digitization for visual presentation: 3D vs. enhanced 2D media o  3D digitization technologies
  • 2. 2 2 Acquiring Visually Rich 3D Models Goal: Build accurate digital models to clone the reality (shape + surface reflection properties) Acquisition methodologies: n  Image-based Rendering o  Panoramic images (2D) o  RTI images (2D) n  Standard CAD modeling (manual process) n  Approaches based on Sampling o  3D scanning (active) o  3D from images (passive) 3 Modelling vs. Sampling o  Modelling n  Manual process [“redraw”] n  Accuracy is unknown n  3D model is usually complete o  Sampling/scanning n  Semi-automatic process [“photography”] n  Accuracy is known n  3D model is usually uncomplete (many unsampled regions) R. Scopigno, 3D Digitization - HW
  • 3. 3 R. Scopigno, 3D Digitization - HW 4 3D scanning devices Many different technologies, just two examples: o  Laser or structured light, Triangulation n  Small/medium scale artifacts (statues) n  Small/medium workspace 20x20 -> 100x100 cm, distance from artifact ~1 m n  High accuracy (>0.05 mm) n  High sampling density (0.2 mm) n  Fast (1 shot in ~1-2 sec) o  Laser, Time of flight n  Large scale (architectures) n  Wide workspace (many meters) n  Medium accuracy (~4-10 mm) n  Medium sampling density (10 mm) n  Slow (1 shot in ~20 min) R. Scopigno, 3D Digitization - HW 5 Active Optical Technologies o  Using light is much faster than using a physical probe o  Allows also scanning of soft or fragile objects which would be threatened by probing o  Three types of optical sensing: n  Point, similar to a physical probe o slow approach, lots of physical movement by the sensor. n  Stripe o  faster: a band of many points passes over the object at once n Other patterns …
  • 4. 4 R. Scopigno, 3D Digitization - HW 6 Stripe-based scanning R. Scopigno, 3D Digitization - HW 7 Optical Technologies - Triangulation How do we compute the 3D coordinates of each sampled point? o  By triangulation, known: n  emitting point of the light source + direction (illuminant or emitter) n  the focus point of the acquisition camera (sensor) n  the center of the imaged reflection on the acquisition sensor plane ( P(a) ) Triangulation is an old, simple approach (Thales-Talete) Issues: precision and price of the system
  • 5. 5 R. Scopigno, 3D Digitization - HW 8 Output: range map R. Scopigno, 3D Digitization - HW 9 Triangulation-based systems An inherent limitation of the triangulation approach: non-visible regions o  Some surface regions can be visible to the emitter and not-visible to the receiver, and vice-versa o  In all these regions we miss sampled points è integration of multiple scans
  • 6. 6 R. Scopigno, 3D Digitization - HW 10 Scanning example R. Scopigno, 3D Digitization - HW 11 Acquisition accuracy o  Depends on sweeping approach … o  … on surface curvature w.r.t. light direction … o  Laser syst.: the reflected intensity can be used as an estimate of the accuracy of the measure
  • 7. 7 R. Scopigno, 3D Digitization - HW 12 Acquisition accuracy o  … on the surface shape nearby the sampled point o  … and on surface reflectance [see Curless Levoy “…Space Time Analysis”, ’95] R. Scopigno, 3D Digitization - HW 13 Optical Tech. – Time of Flight Measure the time a light impulse needs to travel from the emitter to the target point (and back) n  Source: emits a light pulse and starts a nanosecond watch n  Sensor: detects the reflected light, stops the watch (roundtrip time) n  Distance = ½ time * lightspeed [e.g. 6.67 ns è 1 m ] o  Advantages: no triangulation, source and receiver can be on the same axis è smaller footprint (wide distance measures), no shadow effects [Image by R. Lange et al, SPIE v.3823]
  • 8. 8 R. Scopigno, 3D Digitization - HW 14 Optical– Time of Flight o  Optical signal: n  Pulsed light: easier to be detected, more complex to be generated at high frequency (short pulses, fast rise and fall times) n  Modulated light (sine waves, intensity): phase difference between sent and received signal è distance (modulo wavelenght) n  A combination of the previous (pulsed sine) o  Scanning: n  single spot measure n  range map, by rotating mirrors or motorized 2 DOF head [Image by Brian Curless, Sig2000 CourseNotes] R. Scopigno, 3D Digitization - HW 15 3D scanning – raw output data For the user, same type of output data : n  Range map: 2D map of sampled 3D points (640x480 -> 2M - 5M points) n  Can be managed as a point cloud or a triangulated surface chunk
  • 9. 9 R. Scopigno, 3D Digitization - HW 16 Why processing raw scanned data? The acquisition of a single shot (range map) is only a single step in the 3D scanning process, since it returns a partial & incomplete representation dal parziale al totale We need algorithms and software tools for transforming redundandt sampled data into a complete and optimal 3D model  3D Scanning Pipeline R. Scopigno, 3D Digitization - HW 17
  • 10. 10 Note: New approaches appeared that use many redundant & overlapping images to produce results similar to those produced with active scanning devices è 3D from images (passive methods) R. Scopigno, 3D Digitization - HW 18 R. Scopigno, 3D Digitization - HW 19 Questions? o  Contact: Visual Computing Lab. of ISTI - CNR http://vcg.isti.cnr.it r.scopigno@isti.cnr.it