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Presented by : Abhinav Dayal

Presented by : Abhinav Dayal. 20,000 2  1 billion. 5 meters. 20,000:1. 1/4 mm. 1/4 mm. Overview. Motivations push 3D scanning technology tool for art historians lasting archive Technical goals scan a big statue capture chisel marks capture reflectance. ?. ugnetto.

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Presented by : Abhinav Dayal

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  1. Presented by : Abhinav Dayal

  2. 20,0002  1 billion 5 meters 20,000:1 1/4 mm 1/4 mm Overview Motivations • push 3D scanning technology • tool for art historians • lasting archive Technical goals • scan a big statue • capture chisel marks • capture reflectance

  3. ? ugnetto Why capture chisel marks? Atlas (Accademia)

  4. 2 mm Day (Medici Chapel)

  5. 1 mm single scan of St. Matthew

  6. Issues Involved • Scanning • Scanner design • Scanning Procedure • Post Processing • Range Processing • Color Processing • Handling large data sets

  7. Scanner Design • Laser stripe triangulation system • Resolution and field of view • Should capture chisel marks • Reasonable size of resulting dataset • Standoff and baseline • Longer standoff  access to deeper recesses + safe distance from statue • Longer standoff longer baseline prone to miscalibration

  8. Color Acquisition • Single pass • 1D luminaire and 1D color sensor • Cross talk b/w luminaire & sensor poor fidelity • RGB lasers • Large and complex • Separate pass • They used broadband luminaire and separate sensor (Digital Camera – 1520 x 1144 pix res)

  9. Camera Resolution and field • For color to range calibration match respective standoffs • One color per pixel resolution (per range data) • Controlled illumination • Depth of field • DOF > FOV(Z) of range-camera

  10. truss extensions for tall statues 4 motorized axes laser, range camera, white light, and color camera Gantry:Geometric design

  11. calibrated motions pitch (yellow) pan (blue) horizontal translation (orange) Working volume of the scanner • uncalibrated motions • vertical translation • remounting the scan head • moving the entire gantry

  12. Scanning the David height of gantry: 7.5 meters weight of gantry: 800 kilograms

  13. Calibration Range Calibration Color Calibration • Used a planar target with feature points to calculate camera’s intrinsic parameters and to build a per pixel intensity correction table  Their calibration was complex and did moderately well

  14. Scanning procedure • Range Scanning • Typical range involves several horizontally adjacent vertical sweeps or vice versa • Overlap adjacent sweeps by 40% • Overlap adjacent shells by 15% • Color Scanning • Image with spotlight – Image without spotlight = Image with only spotlight

  15. Safety Concerns • energy deposition • Low and not a problem • avoiding collisions • manual motion controls • automatic cutoff switches • one person serves as spotter • surviving collisions • pad the scan head

  16. Range processing pipeline • steps • manual initial alignment • ICP to one existing scan • automatic ICP of all overlapping pairs • global relaxation to spread out error • merging using volumetric method(space carving) • problems • should have tracked the gantry location • ICP is unstable on smooth surfaces

  17. Color processing pipeline • steps • compensate for ambient illumination • discard shadowed or specular pixels • map onto vertices – one color per vertex • correct for irradiance  diffuse reflectance • limitations • ignored interreflections • ignored subsurface scattering • treated diffuse as Lambertian • used aggregate surface normals

  18. Handling large datasets • range images instead of polygon meshes • r(u,v) (special case of displacement map) yields 18:1 lossless compression (run-length encoding) • multiresolution using (range) image pyramid • lazy evaluation • viewer based on point rendering (Qsplat)

  19. Samples with any of its four children at the next finer pyramid level missing deleted Can see holes at coarser resolutions Redden parents of missing children in proportion to the fraction of its missing children Range image at 1/4x resolution Range image at 1/2x resolution Range image at 1x resolution Range image pyramids

  20. Problems faced • Approximated marble as truly lambertian • Many unavoidable holes in the scan • Expensive and bulky gantry • Inadequate calibration • Manual view planning  prone to errors • Manual alignment of successive scans

  21. How optically cooperative is marble? • systematic bias of 40 microns • noise of 150 – 250 microns • worse at oblique angles of incidence • worse for polished statues

  22. Removing the holes • Space carving technique • Create a maximum region of space consistent with the scans • Creates a watertight surface • may lead to surfaces that are less plausible than smoothly extending the observed surfaces. • Recent work based upon Volumetric diffusion (Video) Reference: Filling holes in complex surfaces using volumetric diffusion: James Davis, Steven R. Marschner, Matt Garr, Marc Levoy, Computer Science Department, Stanford University (August, 2001)

  23. Conclusion • provides remarkable background for future developments • Provides massive mesh models • Discusses basic issues involved in 3D scanning • Many lessons learnt • Encouraging many new applications(in areas such as: reverse engineering; industrial design; repair, reproduction, and improvement of machinery; medical diagnostics, analysis and simulation; 3D photography; and building rich virtual environments) QUESTIONS? • This is just a beginning

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