3d scanning pipeline


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3d scanning pipeline

  1. 1. 1 3D Scanning Pipeline Roberto Scopigno, Matteo Dellepiane Visual Computing Lab. CNR-ISTI Pisa, Italy R. Scopigno, 3D Digitization - HW 1 Overview Let us present the processing phases and algorithms required to transform o  a set of redundant & partial sampled dataset (range maps) into o  a complete, optimized 3D model
  2. 2. 2 Planning Acquisition Editing Merging Simplification Texturing Registration 3D Scanning Pipeline MeshLab The  stages  of  the  3D  scanning  pipeline  are   demonstrated  with   o  MeshLab,  an  open-­‐source  tool,   developed  by  CNR-­‐ISTI   o  More  than  300K  downloads  in  2013   o  Video  tutorials  are  a  very  effec<ve   documenta<on  and  training  resource:   n  Delivered  via  YouTube:   http://www.youtube.com/ user/MrPMeshLabTutorials R. Scopigno, 3D Digitization - HW 3
  3. 3. 3 R. Scopigno, 3D Digitization - HW 4 Acquisition Planning o  Selecting the set of views is not easy o  Very hard to scan all the surface o  An example: Scanning the Minerva n  Bronze statue, Archeological Museum Florence (under restoration), 155 cm n  4 acquisitions with different scanners (2000-2002) n  Last scan: Minolta laser scanner (03/2002) o  No. range scans: 297 o  Sampling resolution: ~0.3 mm o  Scanning time: 1,5 days R. Scopigno, 3D Digitization - HW 5 Range map – Registration [1] o  Independent scans are defined in coordinate spaces which depend on the spatial locations of the scanning unit and the object at acquisition time o  They have to be registered (roto-translation) to lie in the same space o  Standard approach: 1. initial manual placement 2. Iterative Closest Point (ICP) [Besl92,CheMed92] MeshAlign 1.0 (C) Visual Computing Group
  4. 4. 4 R. Scopigno, 3D Digitization - HW 6 Pairwise Registration [2] Initial registration with user intervention: Mode 1) The user manually places a range map over another (interactive manipulation) Mode 2) Selection of multiple pairs of matching points ICP R. Scopigno, 3D Digitization - HW 7 Automatic registration o  Many people are searching new automatic approaches to range maps registration o  Our approach works on series of consecutive acquisitions (circular or raster scanning order, overlap existing between rmi and rmi+1) n  Results on a complex X-Y scan of a bas-relief: 163 range maps aligned in 1 h 50 min (unattended) …
  5. 5. 5 R. Scopigno, 3D Digitization - HW 8 Merging Range maps o  Producing in output a point cloud is not acceptable Cons: visualization, data processing, … o  Surface reconstruction: all [aligned] range maps are fused in a single triangulated surface (no redundancy, hopefully no holes) o  But consider that some holes are unavoidable in 3D scanning is the object is complex R. Scopigno, 3D Digitization - HW 9 Merging Range maps Many methods/algorithms proposed: o  Old approach: build a patchwork o  New approaches: n  Fuse the available samples (based on distance field or interpolators) n  Consider samples quality while fusing them (to reduce noise and improve quality of the final mesh) n  Two merging modes: o  Keep holes in the final model o  Produce a water-tight model (no holes, by interpolation)
  6. 6. 6 R. Scopigno, 3D Digitization - HW 10 Optimization: Mesh Simplification o  3D scanning tools produce huge meshes (from 5M faces up to Giga faces) o  Data simplification is a must for managing these data on common computers (PC, internet) o  Standard simplification approach: edge collapse with quadric-based error control (QEM) [GarHecSig97] R. Scopigno, 3D Digitization - HW 11 Managing data complexity o  Multiresolution encoding can be build on top of simplification technology o  Goal: structure the date to allow to extract from the model (in real time) an optimal representation for the current view  view- dependent models produced on the fly o  Note: the screen is limited (2M pixels), take this into account to reduce data representation complexity CNR’s Nexus vcg.isti.cnr.it/nexus/ [“Batched Multi Triangulation”, P. Cignoni et al, IEEE Visualization 2005 + newer ideas]
  7. 7. 7 View-dependent rendering R. Scopigno, 3D Digitization - HW 12 •  Mesh is denser in foreground •  Mesh is more and more coarse as we get farther from viewpoint •  Zones which are outside the view frustum are very coarse Managing data complexity R. Scopigno, 3D Digitization - HW 13
  8. 8. 8 R. Scopigno, 3D Digitization - HW 14 3D scanning cost o  Remarkable evolution since Digital Michelangelo times: increased accuracy & speed, cost reduction Minerva of Arezzo (1st) 150 range maps 1.5 months (2000) Angel, Duomo di Pisa 273 range maps, 7 days (2002) Minerva of Arezzo (4th) 306 range maps, 5 days (2002)  Improving… R. Scopigno, 3D Digitization - HW 15 Questions? o  Contact: Visual Computing Lab. of ISTI - CNR http://vcg.isti.cnr.it r.scopigno@isti.cnr.it