Transcript TU/e

TU/e
DBL Algorithms
Curve and Network Reconstruction
Project coordinator:
Mark de Berg
TU/e
The project in a nutshell
 solve complicated algorithmic problem in a team:
• design, analyze, and implement algorithm
• perform experimental evaluation
 communicate solution
• write paper
• give presentation
 professional skills
• 2PS12: Collaborating 2
• 2PS22: Presenting 2  training by STU
• 2PS42: Reflecting 2
• 2PS52: Planning and organizing 2
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Schedule, deliverables and grading scheme
week
day and time
activity (and location) or deadline
1 (Apr 18‒22)
Wed, hrs 1+2
kick-off meeting (Aud 10)
2 (Apr 25–29)
Fri, 23:59
deadline for first prototype
3 (May 2‒6)
4 (May 9‒13)
5 (May 16‒20)
no plenary meetings or special deadlines
Wed, hrs 2+3+4
hrs 5+6+7
training for mid-term presentations
Fri, 23:59
deadline for interim report
Fri, 23:59
deadline for second prototype
Wed, hrs 5+6
mid-term presentations (Aud 9)
6 (May 23‒27)
no plenary meetings or special deadlines
7 (May 30—June 3)
no plenary meetings or special deadlines
8 (June 6-10)
Wed, hrs 2+3+4
hrs 5+6+7
training for final presentations
9 (June 13-17)
Mon, 23:59
deadline for software submission
Wed, hrs 1+2+3+4
final presentations (Pav J17)
10 (June 20-24)
11 (Jun 37–Jul 2)
percentage of
team grade
interim report: 10%
mid-term presentations: 10%
output of software on test cases: 30%
final presentations: 10%
no plenary meetings or special deadlines
Wed, 23:59
deadline for reflection form prof. skills
Fri, 23:59
deadline for final report
final report: 40%
should be at least 5
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The project in a nutshell
other deliverables include
 group planning and team sheets (every week)
 minutes of meetings with tutor
 test submissions
see course web page for more information
www.win.tue.nl/~mdberg/Teaching/DBL-Algorithms/2IO90-1516-Q4.htm
rest of today’s lecture
 problem description
 more info on the interim and final report
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Surface reconstruction
surface reconstruction
3D scanner
3D printer
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Applications
 custom-fit products
 reverse engineering
 digitizing cultural heritage
Surface Reconstruction
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St. Matthew
The Digital Michelangelo Project
David
Forma Urbis Romae
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The Digital Michelangelo Project
Scanning the David
height of gantry:
weight of gantry:
7.5 meters
800 kilograms
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The Digital Michelangelo Project
Scanning the David
 480 individually aimed
scans
 2 billion polygons
 7,000 color images
 32 gigabytes
 30 nights of scanning
 22 people
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Detail of reconstructed surface
The Digital Michelangelo Project
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3D Scanning Process
The Digital Michelangelo Project
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Some Applications
 permanent archive
 conservation
 unique views of the statues
The Digital Michelangelo Project
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Some Applications
 permanent archive
 conservation
 unique views of the statues
The Digital Michelangelo Project
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Some Applications
 digital museum
 unique views of the statues
 physical replicas
The Digital Michelangelo Project
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Surface Reconstruction
3
9
5
6
surface
reconstruction
8
7
4
2
1
3D scanner
3D printer
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Curve Reconstruction
Your task (part 1): Curve reconstruction
3
2
1
4
5
6
9
7
8
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Curve Reconstruction
Problem Statement
1
Unknown Curve
Point Sample
4
2 3
Reconstruction
Input: set of sample points
Output: reconstruction (here: order of points along the curve)
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Curve Reconstruction
Two variants
single curve
(closed curve)
multiple curves
(open curve)
Task 1: develop algorithms for these two these variants.
 algorithm needs to decide if curve(s) are open or closed
 variant = multiple: algorithm should decide on number of curves
 curve segments in output must be non-intersecting
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Network Reconstruction
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Network Reconstruction
Your task (part 2): Network
reconstruction
Task 2: Develop algorithm for network reconstruction
 segments in output must be non-intersecting
 network must be connected
 extra points may be added
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Software and grading
 software must be written in Java and submitted through Peach
 grading is done by running software on several test instances
• grade based on performance on these test cases
• quality of reconstruction is main criterion
• running time less important, but may also be taken into account
algorithm must terminate within 5 minutes on 10,000 point instances
• software design is not taken into account
See problem description on web page for input and output format, etc
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The interim and final
report
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Groups and tutor
assignment
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Tutor: Jeffrey Aben ([email protected])
Group 1
Group 2
Group 3
Galioulline H.R. (Herman R)
Coonen R. (Rick)
Aerts T.G. (Timothy)
Haghighatkhah P. (Pantea)
Geffen B.A.M. van (Bas)
De Aquino Afonso B.K. (Bruno)
Kroon J.J.H. de (Jari De Kroon)
Kroon A.A.W.M. de (Noud)
Dijk R.A.T. van (Rens)
Kuiper J.A. (Jose)
Leenen M. (Mark)
Janissen B.N.J. (Boy)
Lovett S.J. (Sam)
Verlaek W.M.W.R. (Wouter)
Phung D.T. (Dat)
Popov A.A. (Aleksandr)
Maurix F.G. (Frank)
Group 4
Group 5
Group 6
Bartels J. (Jur)
Boelens W.W. (Wigger)
Bek J.G. (Jochem)
Chirascu D.C. (Dan Cristian)
Maas S.J.W. (Sil)
Loon P.J.A. van (Pieter)
Haak B. (Bart)
Mozalbat G. (Jeffry)
Haenen Y.H.B. (Youri)
Mols J. (Jorik)
Nazeer Mohideen S.S.M.
(Sajid)
Heijden J.J.M.J. van der (Juan)
Paulussen K. (Karsten)
Riel S. van (Sjors)
Ras N.M.J. (Martijn)
Pasliev P.M. (Plamen)
Pits K.R. (Kevin)
Sahoo A. (Anuvab)
Tutor: Jeroen Noten ([email protected])
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Tutor: Ziad ben Snaiba ([email protected])
Group 7
Group 8
Group 9
Hapsari K.I.H.D. (Kresna)
Francescoli P. (Paola)
Berenstein D.M. (David)
Klaren K.R. (Koen)
Golovliov N. (Nikita)
Ma L. (Linna)
Leander J. (Jamiro)
Linde S.H.N. van der (Stef)
Steijn K.R. (Kevin)
Roelofs S. (Stan)
Marin I.A. (Ioana)
Ven M.W.C. van de (Marcin)
Rongen B.F. (Bart)
Pijl M. (Mitchell)
Verhoef C. (Céline)
Wieringa B.L. (Bram)
Sweep R. (Rik)
Wijnands Y.D.P. (Yorick)
Group 10
Group 11
Group 12
Honcoop T. (Tim)
Chang K.X. (Kevin)
Arends S.T.T. (Sven)
Klarenbeek J.R. (Jordi)
Cleijne K.C.G.A. (Kevin)
Li Y. (Yuntao)
Degen B.J. (Bernhard)
Lisenkov I. (Ilija)
Verburg M.G.M. (Michiel)
Chotza T. (Xhulio)
Khaled Mohmed Hassan
Emam Nassar (Khaled)
Luttik D.T. (Daan)
Wit F.E.M. (Fenna)
Man K.W. (Ka-Wing)
Morel R. (Rolf)
Tutor: Michael Yeh ([email protected])