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3D Scanning Pipeline

Roberto Scopigno, Matteo Dellepiane Visual Computing Lab.

CNR-ISTI Pisa, Italy

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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

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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

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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

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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

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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

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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)

…

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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

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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)

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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]

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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]

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View-dependent rendering

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•  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

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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…

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Questions?

o  Contact:

Visual Computing Lab. of ISTI - CNR

http://vcg.isti.cnr.it

r.scopigno@isti.cnr.it