TU/e. DBL Algorithms Curve and Network Reconstruction. Project coordinator: Mark de Berg

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1 DBL Algorithms Curve and Network Reconstruction Project coordinator: Mark de Berg

2 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

3 Schedule, deliverables and grading scheme week day and time activity (and location) or deadline 1 (Apr 21 25) Wed, hrs 1+2 kick-off meeting (Matrix 1.60) 2 (Apr 28 May 2) Fri, 23:59 deadline for first prototype 3 (May 5 9 ) no plenary meetings or special deadlines 4 (May ) day, time TBA training for mid-term presentations group 1 and 2: location TBA group 3 and 4: location TBA percentage of team grade Fri, 23:59 Fri, 23:59 deadline for interim report deadline for second prototype interim report: 10% 5 (May 19 23) Thu, hrs 5+6 mid-term presentations (Matrix 1.46) 6 (May 26 30) no plenary meetings or special deadlines mid-term presentations: 10% 7 (June 2-6) no plenary meetings or special deadlines 8 (June 9-13) day, time TBA training for final presentations group 1 and 2: location TBA group 3 and 4: location TBA 9 (June 16-20) Mon, 23:59 deadline for software submission Thu, hrs 5+6 final presentations (Matrix 1.46) 10 (June 23-27) no plenary meetings or special deadlines output of software on test cases: 30% final presentations: 10% 11 (Jun 30 Jul 4) Wed, 23:59 deadline for reflection form prof. skills Fri, 23:59 deadline for final report final report: 40% should be at least 5

4 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 rest of today s lecture problem description more info on the interim and final report

5 Surface reconstruction surface reconstruction 3D scanner 3D printer

6 Surface Reconstruction Applications custom-fit products reverse engineering digitizing cultural heritage

7 The Digital Michelangelo Project St. Matthew David Forma Urbis Romae

8 The Digital Michelangelo Project Scanning the David height of gantry: 7.5 meters weight of gantry: 800 kilograms

9 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

10 The Digital Michelangelo Project Detail of reconstructed surface

11 The Digital Michelangelo Project 3D Scanning Process

12 The Digital Michelangelo Project Some Applications permanent archive conservation unique views of the statues

13 The Digital Michelangelo Project Some Applications permanent archive conservation unique views of the statues

14 The Digital Michelangelo Project Some Applications digital museum unique views of the statues physical replicas

15 Surface Reconstruction 9 surface reconstruction D scanner 3D printer

16 Curve Reconstruction Your task (part 1): Curve reconstruction

17 Curve Reconstruction Problem Statement Unknown Curve Point Sample Reconstruction Input: set of sample points Output: reconstruction (here: order of points along the curve)

18 Curve Reconstruction Two variants single curve multiple curves (closed curve) (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

19 Network Reconstruction

reconstruction segments in output must be

20 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

21 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

22 The interim and final report

23 Groups and tutor assignment

24 Tutor: Peter van Heck Group 1 (time slota+b) Arjan van den Boogaart Wilco Brouwer Chris Mens Martijn Muijsers Rob Wu Group 2 (time slot A+B) Mike van Drunen Koen van der Heijden Dennis van der Schagt Samuel van der Tempel Inez Wester Group 3 (time slot A+B) Paul van Eekelen Johan Hendriks Raghav Mohan Robert Sevat Emma van de Vreugde Group 4 (time slot A+B) Perry van Wesel Victor Fornade Jesse Verhage Rene Wouters Stevie van der Loo Tutor: Jacco Snoeren (j.p.h.snoeren@student.tue.nl)

3D Scanning. Lecture courtesy of Szymon Rusinkiewicz Princeton University

3D Scanning. Lecture courtesy of Szymon Rusinkiewicz Princeton University 3D Scanning Lecture courtesy of Szymon Rusinkiewicz Princeton University Computer Graphics Pipeline 3D Scanning Shape Motion Rendering Lighting and Reflectance Human time = expensive Sensors = cheap Computer