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
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
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
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
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
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
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
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