PROCESSING STEPS OF TLS FROM POINT CLOUD TO 3D MODEL

Faculty of Forestry, Hydrosciences and Geosciences Institute for Cartography

Semester Thesis

PROCESSING STEPS OF TLS FROM POINTCLOUD TO 3D MODEL OF THE LARGESTICE CAVE ON EARTH, THE EISRIESEN-WELTJeannette MiliusMat.-Nr.: 3328870

Supervised by:

Prof. Dr. Manfred Buchroithnerand:Dr. Bernd HetzeSubmitted on September 11th, 2012

CONFIRMATION

I confirm that I independently prepared the thesis and that I used only the references and auxiliarymeans indicated in the thesis.

Dresden, September 11th, 2012

1

CONTENTS

1 Introduction 12

2 Geology 14

3 Laser Scanning 18

3.1 Terrestrial Laser Scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2 Ranges of Laser Scanner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.3 FARO R� Photon 120/20 Used in the Ice Cave . . . . . . . . . . . . . . . . . . . . . 22

3.4 Phase Measurement Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.5 Cartesian Object Point Coordinates . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4 Point Cloud Registration 27

4.1 Point Cloud Registration with FARO R� SCENE 4.6 . . . . . . . . . . . . . . . . . . 30

5 Processing of the Raw Point Cloud Data 34

5.1 Subsampling and Meshing of the Point Clouds . . . . . . . . . . . . . . . . . . . . 35

5.2 Point Cloud Class Rock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.3 Point Cloud Class Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.4 Geomagic R� . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2

5.5 Meshlab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.6 Modelling of the Geometries of the Ice Cave . . . . . . . . . . . . . . . . . . . . . . 49

6 Texturization of the Cave Model 52

6.1 Texturization of the Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

6.2 Texturization of the Rock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

6.3 Texturization of the Steps, Paths and Handrails . . . . . . . . . . . . . . . . . . . . 56

7 Generating the Whole Fly-Through through the Ice Cave 58

7.1 Camera Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

7.2 Cut and Music . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

8 Conclusion and Result 63

Bibliography 64

Appendix i

A Impressions of the ERW ii

A.1 Impressions of the ERW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

A.2 Table of Geological Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

A.3 Ice Cave Locations difficult to access . . . . . . . . . . . . . . . . . . . . . . . . . . vi

A.4 Analogue Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

B Workflow of the Processing Steps viii

B.1 Workflow of the Processing Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

B.2 FARO R� Scene 4.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

B.3 Processing of the Raw Point Cloud Data . . . . . . . . . . . . . . . . . . . . . . . . xi

B.4 PolyWorks R� . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

B.5 Plug In PointCloud for AutoCAD R� . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

B.6 Texturization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi

Contents 3

C DVD xviii

C.1 DVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix

Contents 4

LIST OF FIGURES

2.0.1 Location of the Alps with the Winding Arch (by www.maps-for-free.com) . . . . . . 15

2.0.2 An Overview of the Tectonic of the Eastern Alps (by Sölva et al. (2005)) . . . . . . . 16

2.0.3 Ice Wall with Light Brown Strata of Cryogenic Calcite Powder . . . . . . . . . . . . 17

3.1.1 Laser Scanning Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.1 Working Principle of TLS (by Feng et al. (2001)) . . . . . . . . . . . . . . . . . . . . 22

3.3.1 Captured Intensity Values of ERW with TLS . . . . . . . . . . . . . . . . . . . . . . 23

3.4.1 Phase Shift Measurement Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.5.1 Laser Scanner Head . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.5.2 Calculation of the Cartesian Object Point Coordinates . . . . . . . . . . . . . . . . . 25

3.5.3 Different Types of Tacheometric Laser Scanner (by Staiger (2003)) . . . . . . . . . 26

4.0.1 Point Cloud Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.0.2 An Artificial Target, here Sphere, used in ERW . . . . . . . . . . . . . . . . . . . . . 28

4.0.3 A Small Section of the Whole Analogue Map, where the Locations of the Scannerare Listed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.1.1 The User Interface with Structure View and Point Cloud . . . . . . . . . . . . . . . . 31

4.1.2 A Small Stone which is Automatically False Determined as a Reference Sphere . . 32

5

www.maps-for-free.com

5.1.1 The User Interface of PolyWorks R� with Selected Points Shown in Red of the PointClass Rock which Have to Separated . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.1.2 Point Cloud Class Step, Path and Handrails . . . . . . . . . . . . . . . . . . . . . . 36

5.1.3 Point Cloud Class with Separated Accessory . . . . . . . . . . . . . . . . . . . . . . 36

5.1.4 Remain of a Reference Sphere, which Has to Selected and Deleted . . . . . . . . . 36

5.2.1 The Meshed Rock Shows a Wonderful Accurate Structure of the Rock Part in theERW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.2.2 Filling Holes in the Rock by Selecting the Area of Interest with PolyWorks R� IMEditmodule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.2.3 Abridged Example of an .obj File of the Project . . . . . . . . . . . . . . . . . . . . 38

5.3.1 Spikes above the Ice Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.3.2 Selected Spikes with Red Dots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.3.3 Ice Surface without Spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.3.4 Triangulated Parts of an Ice Column in the ERW in "Posselthalle" . . . . . . . . . . 40

5.3.5 Spectral Absorption of the Components in the Atmosphere and the AtmosphereItself (by Sørensen (2011)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.3.6 Spectral Albedo of all Kinds of Ice, especially Glacier Ice (by (Reijmer et al., 2001)Reproduced/modified by permission of American Geophysical Union) . . . . . . . . 41

5.3.7 Ice meshed with an Edge Length of 1 m . . . . . . . . . . . . . . . . . . . . . . . . 42



5.3.10 Point Cloud with Approximately five million Points . . . . . . . . . . . . . . . . . . . 43

5.3.11 Point Cloud after Reducing by 70 Percent . . . . . . . . . . . . . . . . . . . . . . . . 43

5.4.1 Detected Geometry Errors with the Tool Mesh Doctor . . . . . . . . . . . . . . . . . 44

5.4.2 Repaired Positions after Usage of the Mesh Doctor . . . . . . . . . . . . . . . . . . 44

5.5.1 Vertex Clustering (comp. Garland (1999) . . . . . . . . . . . . . . . . . . . . . . . . 44

5.5.2 Quadric Edge Collapse Strategy (comp. Tang et al. (2007)) . . . . . . . . . . . . . . 45

5.5.3 Adjust some Parameters for Simplification in Meshlab . . . . . . . . . . . . . . . . . 46

List of Figures 6

5.5.4 Zippering Process in Meshlab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.5.5 Smoothing in Meshlab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.6.1 Clicking in the Area of Points to Create Steps . . . . . . . . . . . . . . . . . . . . . 49

5.6.2 Determine of the Edge Area in the Point Cloud . . . . . . . . . . . . . . . . . . . . . 49

5.6.3 Generating of the Steps by Copying the Modelled Step . . . . . . . . . . . . . . . . 49

5.6.4 Clicking in the Area of Points to Create Cylinder . . . . . . . . . . . . . . . . . . . . 50

5.6.5 Formed Cylinder from the Point Cloud . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.6.6 Connection between Two Cylinders . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.1.1 Material Editor with Settings to Create Ice Texture . . . . . . . . . . . . . . . . . . . 54

6.1.2 Mesh of the Texturized Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

6.2.1 The Original Images of the Ice Cave follow One Another . . . . . . . . . . . . . . . 55

6.2.2 The Model of the Cave lies over the Images like a Stencil . . . . . . . . . . . . . . . 55

6.3.1 A Rendered Image of the Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

6.3.2 The Steps with the Handrails . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

7.1.1 The User Interface of PointoolsTM

View 1.8 Pro . . . . . . . . . . . . . . . . . . . . . 59

7.1.2 A View in the Texturized Ice Cave . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

7.2.1 The User Interface of Adobe R� After Effects R� . . . . . . . . . . . . . . . . . . . . . 61

7.2.2 The User Interface of Adobe R� Premiere Pro . . . . . . . . . . . . . . . . . . . . . . 62

7.2.3 The User Interface of Adobe R� Soundbooth . . . . . . . . . . . . . . . . . . . . . . . 62

A.1.1 Ice Sculptures in the ERW (Permission by Owner of the ERW) . . . . . . . . . . . . iii

A.1.2 View from the "Ice Mammoth" to Hymirburg (Permission by Owner of the ERW) . . iii

A.1.3 View Inside the "Posselthalle" (Permission by Owner of the ERW) . . . . . . . . . . iv

A.1.4 One Staff Member in the Hymirburg (Permission by Owner of the ERW) . . . . . . iv

A.2.1 Table of Geological Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

A.3.1 Very narrow Way-Through complicated the Laser Scanning Process . . . . . . . . vi

List of Figures 7

A.3.2 The more Windings the more Scanner Positions are necessary . . . . . . . . . . . vi

A.3.3 The Ice Itself entails a lot of Winding Formations with unreachable Places . . . . . vi

A.3.4 The Rock forms Itself overlaying Structures which can be seen only from One Sidethat another Scanner Position have to put up . . . . . . . . . . . . . . . . . . . . . . vi

A.4.1 The whole Map with all the 158 Locations of the TLS listed as Red Dots . . . . . . vii

B.1.1 The Workflow of the Processing Steps . . . . . . . . . . . . . . . . . . . . . . . . . ix

B.2.1 Arranging of the Windows during the Registering Process . . . . . . . . . . . . . . x

B.2.2 Because of the Humidity in the Ice Cave the Mirror steamed up. This Scan had toDeleted . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

B.3.1 The whole Point Cloud Model of the Steps and Paths . . . . . . . . . . . . . . . . . xi

B.3.2 A Part of the Point Cloud Class Step, Path and Handrails. The Black Point on theSteps is one Laser Scanner Position, where no Information is given . . . . . . . . . xi

B.4.1 The whole Model of the Point Cloud Class Rock . . . . . . . . . . . . . . . . . . . . xii

B.4.2 It is a Transition from a Point Cloud to a Geometrical Model, which shows that duringthe Meshing Process the Borders are taking over exactly . . . . . . . . . . . . . . . xii

B.4.3 The IMCompress Tool in PolyWorks R� with a Reduction of 30 Percent . . . . . . . xiii

B.4.4 The Point Cloud Class Ice. The Ice is Located on the Wall above Rock and showsSmall Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

B.4.5 The triangulated Model of the Class Ice represents the Point Cloud Class Ice exactly xiii

B.4.6 The Holes under the Steps have to be closed, which the Scanner couldn’t reach . . xiv

B.4.7 The triangulated Model shows that the Holes at the Steps were closed very neat . xiv

B.4.8 A Part of the Point Cloud Class Ice at the "Ice Mammoth" . . . . . . . . . . . . . . . xiv

B.4.9 The Path were closed. Wooden Boards are lying over this Ice Surface . . . . . . . xiv

B.4.10 During the Meshing and Triangulation the PC need a lot of Capacity . . . . . . . . . xiv

B.5.1 A lot of different Bars in one Scene . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

B.5.2 Clicking in the Point Cloud to build a Bar . . . . . . . . . . . . . . . . . . . . . . . . xv

B.5.3 Winding Bars were necessary to connect different Sections . . . . . . . . . . . . . xv

B.5.4 By Clicking the Two Cylinders a rounded Winding Bar was created . . . . . . . . . xv

List of Figures 8

B.5.5 The whole Model of the Generated Steps and Paths . . . . . . . . . . . . . . . . . xv

B.6.1 The Saturation has to reduced to receive a Greyscale Image, a Specular Map . . . xvi

B.6.2 The Black and White Slider have to adjust closer to get more Contrast, a Bump Map xvi

B.6.3 A Scene of the Texturized Ice with a Classical Mesh Smooth . . . . . . . . . . . . . xvii

List of Figures 9

LIST OF TABLES

4.1.1 File Structure of ASCII xyzi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.2.1 Syntax Statements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

10

INDEX OF SYMBOLS

Symbol SI-Unit Meaning

c m/s Speed of lightf Hz Frequency� m Wavelength'

A

radians Phase angle at the beginning'

E

radians Received phase angle after reflection�' radians Phase shiftD m Distance from scanner to object degree Horizontal angle of rotating scanner module of 360 degree⇥ degree Vertical angle of oscillating mirror of 320 degree

ABBREVIATIONS

Symbol Meaning

ERW EisriesenweltNCA Northern Calcareous AlpsGPS Global Positioning SystemIMU Inertial Measurement UnitLiDAR Light Detection and RangingTLS Terrestrial Laser ScanningMLS Mobile Laser ScanningNIR Near-infraredQECD Quadric Edge Collapse Decimation

EXPLANATION

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List of Tables 11

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1 INTRODUCTION

The aspect of the visualisation plays an important role today and in future. On every device the

visualisation part fulfils the clearness for the user nowadays, e.g. Augmented Reality, to get some

information in real-time. With this thoughts, the project of the "3D Surveying and Visualisation of

the Largest Ice Cave on Earth“, the Eisriesenwelt, was originated particularly with regard to the

tourism1.

The Eisriesenwelt is the largest ice cave on earth located in the Tennengebirge Massif in the

East Alps, Austria. With more than 150.000 visitors each year, the cave rank among the most

prominent one. In April 2010 the project starts by 8 students of major cartography of the TU

Dresden and Prof. Manfred Buchroithner, the head of the Institute for Cartography at TU Dresden.

The data capturing for the visualisation process afterwards was done by terrestrial laser scanning.

This is the fastest way to capture data with a high visualisation level of the point clouds itself, which

is a dense accumulation of three- dimensional points. Because of the denseness, the data offers

the possibility of a realistically reconstruction of the surfaces.

Currently laser scanning is a very popular application in different fields like architecture, accident

documentation and for industrial inspection, measurement and reverse engineering applications

etc. One challenge in this project was dealing with the huge amount of data, because the phase

shift scanners produce enourmeous of point data, but the resources of the hardware is limited.

Terrestrial laser scanners, that are working with the phase-measurement principle, determine the

distance to an object, whereupon the ranging instrument defines the phase shift of the emitted

laser signal and the received signal after reflection. Additionally to the captured three- dimen-

sional information of the measured surface of an object, TLSs collect intensity values that depend

primarily on the reflectivity characteristics of the scanned object. Capturing the surface structures

of an object using TLS results in digital point clouds.

This semester thesis gives information on the process of working up the raw data, the point clouds

to a texturized geometry model as well as the different steps of the necessary transformations

between the data formats at varying software packages. Finally a fly through through the ice cave

is generated for the internet portal www.eisriesenwelt.de.

For more information in this topic look in Petters (2012), where the ice surface is calculated

exactly using the three- dimensional model as well as comparisons of this project with other

similar projects.

1Appendix: Some impressive pictures of the ice cave (A.1.1, A.1.2, A.1.3, A.1.4)

Chapter 1 Introduction 13

www.eisriesenwelt.de

2 GEOLOGY

The Alps are situated in a specific location with a winding arch which reaches from Nice to Vienna

(Fig. 2.0.1). They can be separated in three regions: the West-Alps, the Central-Alps and the

East-Alps, which can be divided in different tectonic units. More on that point in Pfiffner (2010).

Figure 2.0.1: Location of the Alps with the Winding Arch (by www.maps-for-free.com)

Compressional tectonics started as early as 100 million years ago (Middle Cretaceous). The

break of Pangea influenced the later originated Alps. The evolution began as the African and

Eurasian continental plates collided and formed an orogenic belt, the Austrian Alps (Christian

and Spoetl, 2010). They are originated in the younger history of the continent in the Cretaceous

and Cenozoic1. The rocks are older than one billion years. The deformation process continued

until about 30 million years ago (Oligocene). In the western part of the Alps the major phase

of uplifting starts and thenceforward this part has been continuously exposed to erosion and

weathering which is a part of karstification. The corrosion and erosion of the limestone is the

consequence of the water flowing in the rock. Because of gravity and geological structures the

water flows down, until it reaches the impermeable strata, where it flows further in horizontally

directions. So the whole system is washed out with water. More information in Christian and

Spoetl (2010) and Audra et al. (2007).

Through the East-Alps extend the Northern Calcareous Alps of which the Tennengebirge Massif

forms a part (see Fig. 2.0.2). The NCA exist of dolomites with a thickness of more than 1000 m

and a slightly folded succession of Trias limestones (Audra et al., 2007). In the Upper Triassic1Appendix: Geological Times Scale (A.2.1)

Chapter 2 Geology 15

www.maps-for-free.com

Figure 2.0.2: An Overview of the Tectonic of the Eastern Alps (by Sölva et al. (2005))

Dachstein Limestone the ERW developed and shows an average rock overburden in its interior

part of approx. 400- 500 m (May et al., 2011).

The Tennengebirge Massif contains 5 cave systems with more than 1000 m deepness and two

of them with more than 30 km length including the ERW with a length of 42 km (Audra et al.,

2002). Several caves in Austria contain thick accumulations of perennial ice (Christian and Spo-

etl, 2010), to which the ERW belongs. The entrance of the ice cave can be achieved in 1641 m

height. Because of the intensive weathering at the entrance a lot of talus will be discover. A rea-

son for that is the combination of breakdown, downslope movement from other rock units above

the cave. Soil slumping, and incorporated plant material creates a characteristic pile of roughly

stratified debris, which is known as entrance talus. This aspect often indicates palaeontological

or archaeological deposits in caves (White, 2007).

The cave is a huge dry system and is related to Miocene conditions. There are a lot of vast

sub-horizontal galleries which are filled with debris. The eroded flowstone shows alternating wet

and dry phases and with the relative lowering of base level, the ERW became perched, drained

and intersected by scarp retreat (Audra et al., 2002).

The ERW exhibits a dynamic ventilation, which is caused primarily on the difference of the tem-

peratures between the plateau of the Tennengebirge Massif and the cave air at the ice free part at

the end of the cave. In winter the cold outside air is sucked in by the lower entrance and provides

consequently the cooling of the ice part. In the warm season the ventilation goes into reverse and

the relatively warmer air from the ice free part at the end of the cave streams to the low entrance,


cools off at the ice part and blows as an strong cold wind outside at the lower entrance. More

information find in Evrard et al. (2011), von Saar (1956) and Audra et al. (2007).

Figure 2.0.3: Ice Wall with Light Brown Strata of Cryogenic Calcite Powder

The main phase of the formation of ice will occur in spring, if the snow melt starts and seeping

water freezes at the cooled ice part of the cave. The ice melting of the ice surfaces inside the

cave happens in summer and autumn. Likewise it is possible that the ice is melting in winter

because of sublimation (the colder outside air stream warms up in the ice cave and soaks up the

humidity) (Spötl, 2008).

This conditions give rise to formation of cryogenic cave carbonates (specific type of speleothem

which is triggered by freezing-induced concentration of solutes). The ice cave is dominated of

highly variable grain morphology because of the karst groundwater chemistry and its freezing rate

upon entering the cave. Slow freezing of water in caves results in the formation of large mineral

grains, with sizes from less than 1 mm to about 20 mm (Žák et al., 2008).

Figure 2.0.3 shows an ice wall with some white to light brown strata of cryogenic calcite powder in

the ERW. The reason of the thickness of the carbonate layers are the harshness of the winter i.e.

cold and long winter lead to intensified sublimation of the cave ice and release finely carbonates

from the ice (Spötl, 2008).


3 LASER SCANNING

Laser Scanning is a very famous measurement technique nowadays. It is a remote sensing

technique based on ranging measurements used in different fields, e.g., for mapping of vegeta-

tion, urban areas, infrastructure, topography and also ice. In Spötl et al. (2008), the method was

proved to be successful in dealing with ice surfaces. In 1958 the first laser was developed, since

then the popularity of this development increases, but just only for military purposes. There are

first studies using the laser in the field of remote sensing, so oceanographic lidar for measuring

chlorophyll concentration and other biological and chemical substances, bathymetry and forestry

applications (Hyyppä, 2011). Airborne Laser Scanning was born after GPS, IMU and scanning

mechanism were attached to laser ranging measurements, first for the military purposes in 1980s,

and later on for surveying.

Terrestrial Laser Scanning differs from Airborne Laser Scanning. The TLS is mounted in a fixed

position, i.e. on a tripod, and the ranging takes place across an angle-of-view (Hyyppä, 2011).

TLS is popular since 1998 within the scope of surveying and its implementation of the first laser

scanning measurements devices. Since that date, it gets more and more popular and in the

meantime it is an established measuring method (Dold, 2010). Another application of laser scan-

ning, which is integrated in the field is the Mobile Laser Scanning, also called Mobile Terrestrial

Laser Scanning. This technique of laser scanning is developing rapidly, because of its huge

amount of possible applications. GNSS and IMU are mounted onboard a moving vehicle. But not

even a moving vehicle is imaginable also going with a laser scanner in heavy attainable regions

with backpack. It filled the gap between the Airborne Laser Scanning and Terrestrial Laser Scan-

ning, because the data collection can be performed in different modes, i.e. the use of continuous

scanning measurements along the drive track (Hyyppä, 2011).

3.1 TERRESTRIAL LASER SCANNING

Terrestrial Laser Scanning is one method for surveying tasks, which allows to acquire easy and

fast complex geometric data from buildings, machines, objects etc. in the form of point clouds

consisting just of points, which can be a very large number of points. It is becoming increas-

ingly popular, because it provides a three- dimensional sampled representation of the surfaces

of objects, whereby the spatial resolution of the data is much higher than that of conventional

surveying methods (Bae and Lichti, 2008). Each position of a point of this point cloud is deter-

mined by the coordinates x, y, z and the intensity value (i) of the returning signal (Staiger, 2003).

Modern scanner will store the colours of the reality in RGB colour.

Chapter 3 Laser Scanning 19

Contrary to the tachymetric surveying, which measures individually, characteristic points with the

help of specific reflectors, the laser scanning provides a surveying of the surface of an object

without using reflectors, which makes the method suitable for hard accessible objects, with a

highly measuring point density in a short time interval. TLS have a limited field of view, thus it

is necessary to collect data from several different locations to acquire the whole surface of the

object. In so doing, a lot of several point clouds occur that must be transformed into a common

coordinate system afterwards (Bae and Lichti, 2008). Therefore different software providers offer

automatically solutions.

The amount of data produced by such systems is huge (at the rate of 0.25 - 1 million pts/s), so

that an automated pre-processing is preferable, because manual processing of the data is very

time-consuming. These are current research topics to deal with the huge amount of points to

decrease the amount of manual work required to produce accurate three- dimensional models

(Hyyppä, 2011).

The result of a measurement with a laser scanner is a so-called point cloud, which typically con-

tains several hundred thousands and up to several millions of points. The data acquisition takes

place very fast and a visualisation of the point cloud in three- dimension is possible after the

acquisition immediately, whereby the positioning of tie points serve as foundation for an accurate

model (Dold, 2010). The orientation, also termed as registration, is normally done by the addi-

tional acquisition of artificial targets or manually using special software packages. The linking of

individual locations resp. the registration of the individual point clouds, the selection of apprecia-

ble measuring data and the interpretation and visualisation of the data are typically steps, which

are done manually after the data acquisition (Fig. 3.1.1).

Positioning of the Tie Points

(Spheres)Data Capturing Registration Processing

Change of View Point

Figure 3.1.1: Laser Scanning Workflow

The number of the available instruments for laser scanning rose only in the last years volatile.

Blais (2004) gives an overview of the developments with three- dimension distance measuring

instruments in the last 20 years, whereby he essentially deals with different measurement princi-

ples and enumerates scarcely 80 commercial provider of such systems (Dold, 2010).


3.2 RANGES OF LASER SCANNER

Different methods are available for the measuring of the distance between the object and the

instrument. So each three- dimensional measuring device can be classified working with different

techniques: Interferometry, Pattern production method, (Laser) triangulation method, Time- of-

flight method and phase shift technology. Equally it is possible to classify the measuring devices

in their maximum measuring distance. Usually an organization of the systems takes place into

three distance ranges:

• Close range (few cm to approx. 2 m measuring range)

• Middle distance range (2 m to approx. 80 m measuring range)

• Far distance range (80 m until approx. 2000 m measuring range)

Instruments in close range are using mainly the interferometry, the pattern production and trian-

gulation method. Numerous of these scanners are conceived for highly exact measurements of

small property. By means of interferometry, surface textures can be measured in the nanometer

range with special measuring devices. The pattern production method is only common in the

close range. Scanned property will be temporally shifted illuminated with different pattern (e.g.

strip projector) and with the help of a camera simultaneously recorded. TLS systems are working

in the middle and far distance range. Instruments using the time-of-flight method or the phase

shift measurement principle, became generally accepted.

The wavelength � defines the maximal explicit measuring distance which is �/2 for a two-way

measurement. The more largely the wavelength, the more largely is also the clarity range and

thus the maximum measuring distance. However the measuring accuracy worsens with larger

wavelengths. Therefore different modulations and wavelengths are used for the ranging (Dold,

2010). The number of the equipment manufacturers of TLS is larger compared with the already

for a long time assigned air-based systems. Likewise the acquisition and operating charges are

smaller compared with the air-based systems, which favoured a fast spreading of these devices.

The main components of the devices are a laser unit, a radiation deflecting system and a receive

unit. Figure 3.2.1 shows a general construction of such a TLS.


digitizing errors of the laser scanning process. Earlier workby Rioux et al. [13] reported that the intensity of the diffuselaser light focused on the photodetector array was the mainfactor affecting the laser digitizing accuracy. A compromisehad to be made between the scanning window size and thecollected laser light intensity. Increased accuracy can beachieved by sacrificing the scanning window size. Randomerrors in the triangulation-based laser scanned data wereprimarily caused by speckles in the laser images due to thecancellation and reinforcement of the light wave amplitude[14]. Tamura et al. [15] examined the systematic errors in a3D laser scanning system as a result of the setting errors ofthe galvanometers, which were used to control the laserbeam direction. A calibration technique was developed toachieve higher measurement accuracy. More recently,Smith and Zheng [16] developed a simulation model forpoint laser triangulation probes. The effect of sensor-to-surface orientation on the scanning errors was demonstratedusing the developed model.The present work attempts to analyze and characterize

the digitizing errors of a commercial laser scanner. Exten-sive experimental work has been performed. The objectiveis to identify the primary scanning process parameters thatcontribute to the digitizing errors and to establish an em-pirical relationship to accurately predict the digitizing errorsfor typical laser scanning operations.

2. The laser/CMM digitizing system

The laser/CMM digitizing system examined in thepresent work consists of a commercial laser scannermounted on a Brown & Sharpe Validator 8202 coordinatemeasuring machine. The CMM drives the laser scannerwithin its work envelope to collect the 3D coordinate dataon a scanned object. The digitized data represent a combi-nation of the 2D coordinate data measured by the laserscanner in the moving scanning plane and the instantaneousposition data of the laser scanner provided by the CMMtranslation device. This data integration is achieved viareal-time data communication between the laser scannercontroller and the CMM controller.The commercial laser scanner carries out the digitizing

tasks based on a patented synchronized scanning technol-ogy. As shown in Fig. 1, a double-sided scanning mirror isused to provide synchronization between projection anddetection. The scanning mirror directs a laser beam onto thescanned surface. The diffuse laser rays from the scannedsurface reflect off the backside of the scanning mirror andare focused onto a charge-coupled device (CCD) array. Asthe scanning mirror rotates, the incident laser beam sweepsout the scanning plane that intersects with the scannedobject surface. From the position of the focused laser imagepoint P on the CCD array and the instantaneous orientationof the scanning mirror, the 2D coordinate of the correspond-ing scanned point on the object surface in the scanning

plane can be determined. The laser scanner requires that thescanned points lie within a preset rectangular scanning win-dow, also known as field of view, in the scanning plane forvalid digitization with acceptable accuracy. As the laserscanner moves in the CMM work envelope, it generates a3D rectangular swept volume based on the scanning win-dow. Valid scanned data will be collected in the portion ofthe object surface that intersects with the rectangular vol-ume.

3. Digitizing error

The laser scanner is an electro-optical device. The 2Dcoordinate of a scanned point in the scanning plane isdetermined based on the principle of optical triangulationfrom the laser image as seen by the CCD photodetector. Asa result, any scanning process parameters that vary the CCDlaser image properties such as intensity and focused areapotentially affect the measurement accuracy. Fig. 2 depictsthe digitizing geometry of a typical triangulation-based laserrange sensor [16]. The laser rays from the laser source arefocused at a fixed point PT, which is often within thescanning window of the laser range sensor. The scannedsurface diffuses the laser rays and the diffuse laser raysthrough the receiving lens are focused to form the corre-sponding laser image on the photodetector. As the positionof the scanned surface changes within the scanning window,the intensity and area of the illuminated laser spot on the

Fig. 1. Working principle of the laser scanner.

186 H.-Y. Feng et al. / Precision Engineering 25 (2001) 185–191

Figure 3.2.1: Working Principle of TLS (by Feng et al. (2001))

3.3 FARO R� PHOTON 120/20 USED IN THE ICE CAVE

The FARO R� Photon 120/20 works in the near- infrared spectrum (NIR) at a wavelength of 785

nm and operates on the phase shift measurement principle. This laser scanner has an accuracy

of 2 mm up to 25 m distance. If the distance increases the accuracy decreases to 4 mm up to

100 m distance. The laser beam moves regularly and systematically over the surface.

The radiated laser light is deflected over flexible mirrors, impinges on the object which reflects

a part of the light back to the scanner and will be finally registered over a receive unit by the

instrument. Over the difference of the phase shift from the sent and received signal, the distance

to the object is determined. Important characteristics for laser scanners are the minimum step

size for the deviation, which defines the maximum resolution as well as the measuring accuracy

of the actual angles, which represent the quality of the measured points. The signal of the laser

beam strews strongly on rough surfaces like rock surfaces and returns relatively much information

to the scanner. However, sending a laser beam on smooth surfaces, e.g. ice, the total reflection

and other physical effects arises, whereby the signal is diverted in the opposite direction of the

scanner and no signal returns. The weak signals are represented in low intensity values and the

strong signals in high intensity values. In this case (Fig. 3.3.1) the ice is nearly black and the

rock, which possess a strong reflection due to their roughness, relatively bright.


Figure 3.3.1: Captured Intensity Values of ERW with TLS

3.4 PHASE MEASUREMENT PRINCIPLE

The laser light at the phase shift measurement principle is continuously sent (for several seconds),

whereby harmoniously waves are emitted. The emitted waves and the phase difference is used

to determine the distance D. From the emitted waves with an angle 'A

of the beginning phase

and the receiving signal after reflection at the scanner with an angle 'E

, the phase shift �' will

be measured (see this information in Fig. 3.4.1). The advantages of the phase shift measurement

principle are the very high measurement speed, higher precision and thus the resolution.

Emitter

Receiver

1!

2!3!

φA

φE

"!

Distance D

Obj

ect

Phas

e m

easu

rem

ent

Figure 3.4.1: Phase Shift Measurement Principle


For the determination of the distance D, the phase shift between the emitted and the received

signal, �' given in radians (Equ. 3.2), is evaluated.

2D = N ·�+�� (3.1)

2D =

✓N ·�+

✓(�') · �

2⇡

◆◆(3.2)

D =

1

2

✓N ·�+

✓('

E

� 'A

) · �

2⇡

◆◆(3.3)

in this case: D =

1

2

✓3 ·�+

✓('

E

� 'A

) · �

2⇡

◆◆(3.4)

In equations 3.1, 3.2 and 3.3, the wavelength is depicted by � in meter and the number of full

wavelengths between the sensor system and the reflecting object surface by N. The method is

especially useful for the measurement of complex continuous geometries at a limited distance.

But TLS is not error free like each measuring procedure, different factors affects the measurement

and leads to a falsified result of measurement. A comprehensive composition from literature to

the investigation and analysis of errors when measurements with a TLS as well as a detailed

description of the sources of error are to be found in Reshetyuk (2006).

3.5 CARTESIAN OBJECT POINT COORDINATES

The horizontal angle ( ) is given by the rotation angle of the laser scanner about it’s vertical axis,

measured by an angle encoder. The vertical angle (⇥), measured by a second angle encoder,

is defined by the rotation angle of the reflecting mirror which deflects the laser beam through

the space (Fig. 3.5.1). The internal processor calculates the distance (D). With all parameters

mentioned before the cartesian coordinates of every point can be determined (Fig. 3.5.2).


e

^

Laser beam Oscillating mirror with 320° vertical view

Rotating scanner with 360° horizontal view

Figure 3.5.1: Laser Scanner Head

z

y

x

P(x;y;z)

^

e

D

Figure 3.5.2: Calculation of the Cartesian Object Point Coordinates

Hence the cartesian object point coordinates x, y and z can be calculated by the following trans-

formation:

x = D · cos(⇥) · cos( ) (3.5)

y = D · cos(⇥) · sin( ) (3.6)

z = D · sin(⇥) (3.7)

Usually to the coordinates still instrument-specific correction terms are attached, which mechan-

ical constants and atmospheric influences consider. Additionally to each point the intensity value

which shows the signal strength of the laser beam reflected by the object is registered in the file.


The result is a point cloud which shows a realistic representation of the environment. A distinction

of TLS is given over the field of view of the devices (Fig. 3.5.3). That means the deflection of

the laser beam has a specific footprint in the measuring range. Thus it is differentiated between

panorama, hybrid and camera scanners (Staiger, 2003). Panorama scanners have the advan-

tage that the range of vision is reduced only by a minimum shading range (causes by the housing

of the instrument).

Hybrid scanners cover the entire horizontal range likewise with their field of view, however the

vertical field of view is limited by the assigned deflection mechanics. Panorama as well as hybrid

scanner cover the horizontal range of 360˚ by a mechanical turn of the laser scanner head or the

entire instrument. Camera scanners will be pointed manually at the interested object, because

they don’t have outwardly moving components. Thus the field of view is restricted to a limited

cutout. Depending upon the kind of scanner another radiation deflecting system is usually used.

TS12 Positioning and Measurement Technologies and Practices Rudolf Staiger 12.3 Terrestrial Laser Scanning – Technology, Systems and Applications 2nd FIG Regional Conference Marrakech, Morocco, December 2-5, 2003

4/10

1.3 Types of tacheometric Laser Scanners Today the scanner systems on the market can be divided in three different types (Fig. 4):

- Camera Scanner: a limited Field of View (FOW) of e.g. 40x40°, comparable to a Pho-togrammetric Camera, is scanned. Examples are CYRA 2500 (LEICA) and ILRIS 3D (OPTECH). This type is optimized for a view from outside onto the object(s). There-fore a long range distance measurement device is useful.

- Panorama-Scanner: the field of view is only limited by the base of the instrument

(incl. the tripod). This type is designed for indoor purposes, esp. the digitization of rooms, facilities, etc. Examples are Imager 5003 (ZOLLER & FRÖHLICH) or CAL-LIDUS (CALIDUS PRECISION).

- Hybrid Scanner: One rotation axis is without restrictions (often the Horizontal

Movement) the second rotation axis is – due to the use of mirrors - limited, e.g. to 60°. GS 200 (MENSI) and LMS Z 360 (RIEGL) represent this group.

Tacheometric Laser-Scanner

Hybrid-Scanner Camera-Scanner

Panorama-Scanner

Figure 4. The different types of tacheometric scanners 1.4 Software Each manufacturer offers product-specific software for the acquisition of the data. The point clouds are stored in an internal data format. Therefore the customer needs special software which allows to read and treat the data afterwards for the treatment.

Figure 3.5.3: Different Types of Tacheometric Laser Scanner (by Staiger (2003))

Typically rotating mirrors are assembled in panorama scanners, mirror polygons with hybrid and

twin axis mirror systems with camera scanners. As measurement principle with the laser scan-

ners the time-of-flight method became generally accepted, in individual cases even the laser

triangulation method is employed as well (Dold, 2010).


4 POINT CLOUD REGISTRATION

The result of the laser scanning process of a surface presents multiple scans from different per-

spectives. The original coordinate system that a TLS works with presents a local spherical polar

coordinate system. One major aim and one of the important step in processing of the data is the

transformation of the diverse point clouds into one common coordinate system to get a represen-

tation of the whole object (Fig. 4.0.1). This working step is called point cloud registration.

global

localz

yx

localz

yx

localz

yx

Figure 4.0.1: Point Cloud Registration

The registration of multiple point clouds will be carry out semi-automatically. At least three identi-

cal points for each point cloud have to serve as reference points, which stretch out a coordinate

system. By selecting three pairs of corresponding points, automatically iterative algorithms are

used. Therefore artificial targets like checkerboards or spheres (Fig. 4.0.2) can be used.

Figure 4.0.2: An Artificial Target, here Sphere, used in ERW

Chapter 4 Point Cloud Registration 28

In comparison there is the possibility to use naturally control points, which are generated from the

point cloud afterwards (Wunderlich and Ingensand, 2004).

The complete capturing of objects is usually possible if the objects are scanned from different

points of view. Obstacles1, which were enormous by the rock formations in the cave, can be

avoided by changing the point of view. With a total length of one kilometre, a total amount of

158 point of views were necessary to capture the whole ice filling part of the cave. Therefore an

analogue map2 (Fig. 4.0.3) was very helpful for the allocation of the different TLS locations for

the registration process afterwards.

Figure 4.0.3: A Small Section of the Whole Analogue Map, where the Locations of the Scanner

are Listed

The point clouds were captured with one fourth of the optimum resolution i.e. with 244 000 points

per second. One capturing of a scan takes three and a half minute, so that each scan consists of

approximately 50 million points. The captured scans are saved in .fls format, which is nowadays

a valid format and is supported by many programs.

1Appendix: Interesting winding formations of the ERW (A.3.3, A.3.2, A.3.1, A.3.4)2Appendix: The whole analogue map with all the listed locations of the TLS (A.4.1)


4.1 POINT CLOUD REGISTRATION WITH FARO R� SCENE 4.6

The FARO R� SCENE 4.6 software from FARO R� Europe GmbH & Co. KG offers point cloud

registration with the FARO R� Laser Scan format .fls on the basis of reference points that can

be either reference spheres or reference planes. The first processing step comprehends the

detection and allocation of the spheres (diameter of 7,25 cm) and the labelling with an obvious

name. In the appendix B.1.1 of this thesis a workflow of the processing steps is attached, which

illustrates the explained workflow on the following pages. This chart gives an overview about the

used software and the used file formats.

Once the spheres have a distance to the scanner of less than 18 meters while scanning with a

resolution of 1/4 and a good visibility during the scanning process, this application automatically

detects the spheres with great accuracy. Therefore the spheres should have at least 10 to 15

pixels in the scan. If this is not the case, the spheres have to detect manually which is still

time consuming. A lot of scientific publications about registration was already made shortly after

the development of the laser scanners and approaches for the automatic registration of three-

dimensional datasets are continuously discussed in the research. Nevertheless the registration in

the software of the manufacturers of TLS is possible only with manual interaction. The developed

algorithms for registration permit at present no reliable and fully automatic registration of point

clouds. Another important issue is the definition of the index for the spheres. It is important that

the same sphere in different laser scans get the identical index. If the index of one sphere has

varying indices in diverse laser scans, the registration in the software SCENE fails. The user

interface of SCENE appears very well-arranged. On the left side in the structure view the scans

in the workspace are listed and the particularly point cloud is visible (Fig. 4.1.1).

The user interface offers many opportunities to interact with the laser scan data in different types

of view like the planar view, the quick view or the three- dimension View. Very important for such

huge amount of data (50 millions points for each of the 158 point clouds) is the option to adjust

the number of the displayed scan points for a better performance on the computer. Another very

useful implementation is the mutable structure of the windows. To combine the same spheres it

is necessary to see the spheres of an area on the screen simultaneously. Therefore the windows

can be arranged one below the other3.3Appendix: Mutable structure of the windows to detect spheres manually (B.2.1)


Figure 4.1.1: The User Interface with Structure View and Point Cloud

The loaded point clouds in the structure view will be saved in a project file format .fws (FARO R�

workspace). In the folder where the workspace file is saved, SCENE maintains another folder

called Scans. This folder contains all the scan files that are part of the workspace, which is ab-

solutely necessary to be placed in the same directory like the file FARO R� workspace .fws. In the

structure view the point clouds will be loaded and registered with each other afterwards.After the

automatically detecting of the spheres, they will be appear behind the small plus sign in the struc-

ture view.Because of the absolutely necessary coordinate system in each point cloud, a minimum

number of three spheres, which define the x, y and z- coordinate axis, are necessary. Particu-

lar it is necessary that the distance between the reference points should not be smaller than 1

m and not larger than 18 m. Additionally the spheres should be positioned in different heights

to determine the three- dimensional coordinate system. It is better to have a higher number of

object matches per scan to strengthen the correspondence, which increases the accuracy of the

registration process.

The structure view of the spheres is implemented as an traffic light procedure which is a part

of the Scan Manager. This tool displays the current status of the registration and indicates the

quality of the placement results. Green means a very good accuracy smaller than one millimetre,

yellow between 1 and 5 millimetre and a red sphere an accuracy of more than 5 millimetre. The

green "c“ shows a conjunction of the sphere in several point clouds. If the traffic light appears to


red at one sphere, the sphere is not attachable with other point clouds. This kind of spheres have

to deleted to be able to stationing the scans, so that is absolutely necessary to work with enough

spheres during the field work. Sometimes the software detects small features as a sphere (Fig.

4.1.2). In most cases these spheres will appear in red, because of the rough irregular surface.

They have to be detected and deleted, to carry out an highly accurate registration.

Figure 4.1.2: A Small Stone which is Automatically False Determined as a Reference Sphere

Because of the huge cave system, the maximum distance between the scanner and reference

spheres could not be kept all the time, so that several traffic lights appear red. Therefore, steps

and paths inside the cave, artificial control points, were even used and detected as reference

planes. This has to carry out manually for each point cloud, too. The more equally reference

planes and spheres are connected in different point clouds to each other, the better will be the

accuracy of the whole model (Milius and Petters, 2012). Unfortunately during the scanning pro-

cess some errors4 occurred. The humidity in the ice cave sometimes led to steaming up the

derivating mirror. Therefore it is very important to check the scans just on location. Such defec-

tive scans have to deleted and should not be used.

4Appendix: Defective point cloud, which had to deleted (B.2.2)


File Format xyz

Concluding the registered point clouds are exported in a .xyzi file format with maximally five

million points each to handle the data for the next steps. Each row of the scan file contains one

scan point (xy- coordinate, z-elevation) with an intensity value which represents the reflection

value of the scanned surface of the object. The values in the last column represents the intensity

values from 0 (black) to 255 (white). In table 4.1.1 the file structure of the ASCII xyzi format is

exemplary listed.

Newly scanners like the FARO R� Focus 3D capture the reality immediately in color, because of

the integrated camera. That means that the ASCII File contains RGB values for each point. If

image overlay data is available, the point cloud may be delivered in an xyziRGB format.

Table 4.1.1: File Structure of ASCII xyzi

x y z i

-101.53530000 65.26230000 -12.50800000 104

-101.55540000 65.25500000 -12.51980000 114

-101.56240000 65.25350000 -12.52350000 113

-101.55340000 65.26620000 -12.51520000 83

-101.56530000 65.26140000 -12.52240000 100

-101.55890000 65.27550000 -12.51480000 113


5 PROCESSING OF THE RAW

POINT CLOUD DATA

The next step in the processing of such point clouds entails doing to connect the particularly

points to one surface to capture some texture for the virtual fly through through the ice cave.

Therefore different software packages were used for the further processing of the raw data.

5.1 SUBSAMPLING AND MESHING OF THE POINT CLOUDS

For the subsampling and meshing of the data the software package PolyWorks R�Inspector for

point-based adaptation was used. Unfortunately still just the commercial software packages pro-

vides the best results by handling with point clouds. It is a powerful software solution that is

useful with high-density point clouds and contains different modules to work with them. For the

project the modules IMInspect and IMCompress were useful. They permits an easy and precise

navigation through the registered point clouds.

Figure 5.1.1: The User Interface of PolyWorks R� with Selected Points Shown in Red of the Point

Class Rock which Have to Separated

IMInspect offers the possibility to divide the points into different groups. The different points can

be separated from each other and put into different layers, which can be switched on and off.

Figure 5.1.1 shows the interface of PolyWorks R� IMInspect. It has a similar structure like the

Chapter 5 Processing of the Raw Point Cloud Data 35

software FARO R� SCENE. On the left side the tree view with loaded point clouds is visible and in

the large subwindow the editing object.

The points were separated manually by the different intensity values, in which the light-coloured

points were allocated to the layerclass rock and the darker points to the layerclass ice. Other

classes were created to allocate the remaining points of the handrails and the gangways as well

as the steps1(see Fig. 5.1.2, Fig. 5.1.3).

Figure 5.1.2: Point Cloud Class Step, Path

and HandrailsFigure 5.1.3: Point Cloud Class with Sepa-

rated Accessory

The subdivision was necessary, because the different point areas have to obtain differently and

every point class gets another texture afterwards [see Chapter 6]. Another point was to clear of

the remains of the scanned spheres from the point clouds, which is an important pre-processing

step before the meshing, because it can falsify the meshed surface.

Figure 5.1.4: Remain of a Reference Sphere, which Has to Selected and Deleted

1Appendix: The image shows the point cloud class steps, paths and handrails (B.3.1, B.3.2)


5.2 POINT CLOUD CLASS ROCK

The meshing of the rock surface was really complicated, because of this huge amount of data

points. It was realised by IMInspect with its tool Wrap Mesh. That the tool was even working, the

point clouds of the class rock firstly have to reduced. Each .xyz file was separately processed

and were subsampled by 90 percent. After the subsampling paired point clouds were merged for

the meshing process. For the meshing with Wrap Mesh the maximum edge length was assigned

between three and six meters, because this can differ from point cloud to point cloud. Figure 5.2.1

shows the meshed rock2 with its structure in detail with edges, nooks as well as small features3.

Figure 5.2.1: The Meshed Rock Shows a

Wonderful Accurate Structure of the Rock

Part in the ERW

Figure 5.2.2: Filling Holes in the Rock by Select-

ing the Area of Interest with PolyWorks R� IMEdit

module

The point clouds of the three parts of the cave (top, middle, bottom) were merged to three data

files. The post-processing was necessary to sort the resulted triangles of the meshing process.

With the tool IMCompress the huge amount of created triangles were reduced by 30 percent4.

After the meshing some holes can occur. Therefore the IMEdit module can be very useful to

close the holes, because the module offers the opportunity to edit the polygonal model.

Afterwards the meshed points were exported in wavefront .obj file format.2The appendix contains the whole meshed rock model (B.4.1)3Appendix: The image shows the conversion from a simple point cloud to an elaborate meshed polygon model (B.4.2)4Appendix: Parameters in the IMCompress tool (B.4.3)


File Format wavefront obj

# File generated by InnovMetric Software Inc.# 1754561 vertices, 3495377 triangles

mtllib Eisriesenwelt_050-054_107-113_Stone_3495377triangles.mtl

g defaultv 111.495697 -153.945908 -24.260401 219 219 219v 111.500603 -153.938705 -24.270000 238 238 238v 111.491203 -153.930298 -24.270399 238 238 238v 111.484497 -153.943497 -24.254499 243 243 243v 111.477203 -153.932800 -24.262699 247 247 247vn 0.084487 0.824586 0.559393vn 0.255895 0.515273 0.817931vn 0.369170 0.549603 0.749433vn 0.197265 0.706613 0.679547vn 0.104007 0.564860 0.818606g defaultusemtl material_1f 1//1 2//2 3//3f 4//4 3//3 5//5f 6//6 7//7 5//5f 8//8 9//9 10//10f 6//6 11//11 10//10f 6//6 12//12 11//11

Figure 5.2.3: Abridged Example of an .obj File of the Project

The wavefront .obj file format is a very simple file format that represents three- dimensional ge-

ometry. It can be written in ASCII (.obj) or binary format (.mod). Every position of each vertex,

normals, and faces that make each polygon defined as a list of vertices, and texture vertices. The

file structure stands out due to a lot of possibilities to describe a geometry. In figure 5.2.3 the

vertex data: geometric vertices (v) and the normals of the vertexes (vn) as well as the elements:

the faces (f) are shown. The vertex data is represented by vertex lists; one for each type of vertex

coordinate and their weights (w). A right-hand coordinate system is used to specify the coordi-

nate locations. The following syntax statements in tab. 5.2.1 are used in figure 5.2.3:

Table 5.2.1: Syntax Statements

v x y z w

vn x y z w

f 1//1 2//2 3//3 4//4

The last term in tab. 5.2.1 its an description of the element faces. If there are only vertices and

vertex normals for a face element (no texture vertices), two slashes (//) had to added.


5.3 POINT CLOUD CLASS ICE

For the subsampling and meshing of the point class ice even the module IMInspect was used. For

this huge amount of data of the point cloud class ice the module IMCompress was not working.

Because of this reason the subsampling was carried out with the subsampling tool in IMInspect.

The point cloud was subsampled by 70 percent accidentally. Beforehand the outliers above the

ice surface were deleted manually. In figure 5.3.1 they appeared above the ice surface as spikes

[comp. Chap. 3.3], then they have been selected (Fig. 5.3.2) and deleted. It followed a smooth

ice surface without spikes (Fig. 5.3.3).

Figure 5.3.1: Spikes above the Ice Surface Figure 5.3.2: Selected Spikes with Red Dots

Figure 5.3.3: Ice Surface without Spikes

In contrast to the point class rock, the point class ice was triangulated5 based on different planes,

which allows a separation of the objects within the spatial planes xy, yz and xz (Fig. 5.3.4).5More examples about ice triangulation in Appendix (B.4.4, B.4.5, B.4.6, B.4.7, B.4.8, B.4.9)


Figure 5.3.4: Triangulated Parts of an Ice Column in the ERW in "Posselthalle"

Because of large holes in the ice surface that caused by absorption and refraction as well as total

reflection of the laser beam on patches of smooth ice, the edge length had to adapted. Owing

to the smooth ice surface no scattering occurs and in the most unfavourable case the incident

laser beam will disappear and no signal returns to the scanner. If the laser beam not disappear

completely, the scattering occurs and the scanner receives a small signal, which results in dark

intensity values [see Chapter 3.1].

In figure 5.3.5 the atmospheric windows of the components of the atmosphere and the atmo-

sphere itself are represented. The red line shows the wavelength range in which the FARO R�

Photon 120/20 works. It is quite good visible that the laser scanner strikes the atmospheric win-

dows of all the components in a very acceptable way for this special properties in the project.

Figure 5.3.6 shows glacier ice with low albedo at this wavelength (red line) with the result that

the mostly of the incident laser beam will be absorbed and a very small signal returned to the

scanner, which can be perceived as glacier ice (More information in Martin (2010), Greuell and

Oerlemans (2004) and Reijmer et al. (2001)).


Figure 5.3.5: Spectral Absorption of the Components in the Atmosphere and the Atmosphere

Itself (by Sørensen (2011))

Figure 5.3.6: Spectral Albedo of all Kinds of Ice, especially Glacier Ice (by (Reijmer et al., 2001)

Reproduced/modified by permission of American Geophysical Union)


It was necessary to choose a small edge length to keep the details of the ice figures (0.6 m),

whereas a larger edge length was useful to close the large holes in the ice fields (15 m). The

figures 5.3.7, 5.3.8, 5.3.9 show a part of the ice meshed with different edge lengths.

Figure 5.3.7: Ice meshed

with an Edge Length of 1 m





Figure 5.3.7 shows the meshed ice with an edge length of 1 m, whereby the holes weren’t closed

and the edge length was adapted to small.The same problem in figure 5.3.8, where the holes

were closed with an edge length of 2 m but not satisfying enough. In figure 5.3.9 all holes were

closed with an edge length of 3 m and the details were kept. This small differences in adapting

the edge lengths could be have such an enormous influence and should be maintain carefully.

Thinning of the huge amount of data was unavoidable for further processing. Each point cloud of

the class ice consists of five million data points (Fig. 5.3.10) exported in the .xyzi file format from

FARO R� Scene [see Chap. 4.1], which were reduced by 70 percent to one million data points

(Fig. 5.3.11). At the end of the processing step of the meshing the meshed point class ice was

exported in wavefront .obj file format [see Chap. 5.2]. Despite the use of a powerful PC6 the

meshing and triangulation of the point clouds was a lengthy process. All in all it took about 24

hours per point class to obtain a realistic geometry from the point clouds (Milius and Petters,

2012).

6Appendix: Full capacity of the PC during the meshing process with 32 GB RAM and four cores (B.4.10)


Figure 5.3.10: Point Cloud with

Approximately five million Points

Figure 5.3.11: Point Cloud after

Reducing by 70 Percent

5.4 GEOMAGIC R�

The whole separate meshes of the class rock were merged in the software package Geomagic R�

Studio with the merging tool. During the merging process the whole class rock were compressed

up to five million triangles, where small features were preserved. The tool MeshDoctor gives the

opportunity to analyse the quality of the meshed model. Figure 5.4.1 shows a part of the stone,

where the incorrect geometries were detected. This can be holes, intersecting triangles, spikes

(occur when three or more neighbouring triangles form a pyramid, or a point above or below the

other neighbouring triangles (comp. Fig. 5.3.1)) and highly-creased edges (this are adjacent

triangles that share an edge at sharp angles. They are similar to spikes, but the triangles don’t

rise above or below the surrounding surface. They are typically folded on top of one another onto

the same plane as neighbouring triangles).

In figure 5.4.2 the mesh doctor was applied. However there are just still some incorrect geome-

tries, but reduced. It was very important to use the mesh doctor not to much, because of the auto

repair tool the geometry of the model can be changed. The rock exhibits a lot of edges, corners

and asymmetric placements which are deliberate and could be disturb by utilize the mesh doctor

quite often.

Unfortunately the triangles or rather the meshed model of the rock was still too large for the

further processing of the texturizing, so that the rock had to compressed again with the software

Meshlab [see Chap. 5.5] up to two million triangles.


Figure 5.4.1: Detected Geometry Errors with

the Tool Mesh Doctor

Figure 5.4.2: Repaired Positions after Usage

of the Mesh Doctor

The full skills which Geomagic R� Studio offers were not used within this project. Geomagic R�

Studio is one of the brand leader in the meshing process but for the requirements in this project

with a specific needed performance insufficient.

For more information in the mesh processing with all mathematical backgrounds and current

reasons Botsch et al. (2010) is recommended.

5.5 MESHLAB

The triangles of the separate meshed class ice have to reduced again by 80 percent with the open

software Meshlab. With Meshlab it is possible to deal with large data. After the subsampling the

mesh was remeshed using the Quadric based Edge Collapse Decimation. This simplification

algorithm gives better result than clustering which uses a uniform grid to collapse all the vertices

falling in the same grid onto a single vertex (Fig. 5.5.1). Clustering is a very fast algorithm, but

not very accurate so that topological inconsistencies are a main problem.

Bernd Bickel, TU Berlin, 2012

CG

21

Vertex Clustering

Cluster Generation Computing a representative Mesh generation Topology changes

• If different sheets pass through one cell • Not manifold

Figure 5.5.1: Vertex Clustering (comp. Garland (1999)


On the left top part of the figure 5.5.1, it can be detected that all three vertices are contracted.

So the face is changed to one point. This is a disadvantage of this method. This point will never

disappear while not starting a new run with a new grid with a larger grid width. The small triangle

in the middle of the top will be connected with the large polygon. This can be an advantage if

it is wanted to connect independent parts of the model to a big one. Should be preserved the

fundamental structure of the object, so the Vertex Clustering is unsuited.

k. Since all the other triangles have no change except thetriangles adjacent to the two points i and j, which are thetwo vertices of the deleted edge. We can deduce fromEq. (7) that the moment difference of order up to Mbetween FN!2 and FN to be given

ESMD "XM

p"0

Xp

q"0

Xq

r"0

X

v2 Ti#Tj!Teij

! "mp!q;q!r;rS0 T n$ %

0

B@

#######

!X

v2Tk

mp!q;q!r;rS0 T n$ %

1

CA

#######, $8%

where Tv denotes the triangles adjacent to a vertex v andTeij

denotes the two triangles adjacent to the edge eij.Eq. (8) shows that the computation of adjacent trianglemoments is taken instead of the whole object trianglemoments.

This principle can be extended to vertex or face removalmethod by only taking their respective triangle changescaused by each operation into account.

3.2. Volume moments-based metric

The volume moment of order k " k1+k2+k3 of a 3Dcompact body P is defined by:

mk1k2k3V P$ % "Z

P

xk1yk2zk3 dxdydz, (9)

where the integral is taken on the volume of P.Notice that m000V(P) is the volume of the model P which

was used by Alliez et al. [17] as a simplification metric intheir method [Eq. (2)]. The simplification cost function byusing the volume moments is similar to that of the surfacemoments. It is defined by

VMD "PM

p"0

Pp

q"0

Pq

r"0mp!q;q!r;rV P$ % !mp!q;q!r;rV

0 P$ %! "

#####

#####;

(10)

where mk1k2k3V $P% and mk1k2k3V0$P% denote the

(k1+k2+k3)th order of volume moments defined on thevolume P and simplified mesh P0, respectively. ComparingEq. (10) with Eq. (2), it can be seen that Eq. (2)corresponds to a special case of Eq. (10) (with M " 0).

Tuzikov et al. [18] also proposed a fast algorithm forcomputing the volume moments. The computation methodof volume moments is similar to that of surface moments.Similar to Eqs. (5), (6), the corresponding formulas forvolume moments-based simplification are as follows:

mk1k2k3V P$ % "Z

P

xk1yk2zk3 dxdydz "X

i

mk1k2k3V Ti$ %,

(11)

mk1k2k3V T$ % "Aj jk1!k2!k3!

k # 3$ %!

&X

kij$ %2z

Q3j"1

P3i"1kij

$ %!

$ %

Q3i;j"1 kij !! "

Y3

i;j"1A

kij

ij , $12%

where Aj j is the determinant of A. Using Eq. (11), Eq. (10)becomes

VMD "XM

p"0

Xp

q"0

Xq

r"0

#####

&X

i

mp!q;q!r;rV Ti$ % !X

i

mp!q;q!r;rV0 Ti$ %

!#####

$13%

For iterative mesh simplification methods, only some localmodifications present to each iteration. Similar to that ofsurface moments-based metric, we take edge collapseoperation to illustrate the simplified form of Eq. (13) fora series of decimation methods. We can deduce from Eq.(13) that the moment difference of order up to M betweenFN and FN!2 is given by:

EVMD "XM

p"0

Xp

q"0

Xq

r"0

X

v2 Ti#Tj!Teij

! "mp!q;q!r;rV Tv$ %

0

B@

#######

!X

v2Tk

mp!q;q!r;rV Tv$ %

1

CA

#######, $14%

where EVMD denotes the global volume moment differ-ence of an edge collapse operation, which is called the costfunction. As mentioned previously, this metric can be easilyextended to vertex- or face-removal operation.

4. Experiments

The experiments were performed on a PC Pentium 42.66GHZ CPU with 512MB RAM, running on WindowsXP operating system. Visual C++ and OpenGL weretaken as development tools. The results for the cow model(2904 vertices and 5804 triangles) and the North Americamodel (2025 vertices and 3872 triangles) are presented. Theoriginal models are shown in Fig. 2.

ARTICLE IN PRESS

Fig. 1. Process of edge collapse from eij to vertex k.

H. Tang et al. / Computers & Graphics 31 (2007) 710–718 713

Figure 5.5.2: Quadric Edge Collapse Strategy (comp. Tang et al. (2007))

The QECD is one variation of the edge collapse algorithm, which is based on quadratic error met-

rics by Michael Garland and Paul Heckbert (More information in Garland and Heckbert (1997)).

This method holds much promise because it is fast and reasonably accurate. The base operation

is the edge collapse. An edge collapse is an operation that reduce an edge into a single vertex,

i.e. two vertices (i,j) are merged into one (k) (see Fig. 5.5.2). When this is done all edges and

faces, connected to the removed vertices, has to be reconnected to the new vertex (Gahm, 2010).

Meshlab offers the opportunity to adjust the level of simplification with QECD by parameters (Fig.

5.5.3).


Figure 5.5.3: Adjust some Parameters for Simplification in Meshlab

• Target Number of Faces: If there consists target-settings to get a certain amount of faces.

• Percentage Reduction: This can be done also in percentage so that the first field don’t have

to be filled. In this case the mesh was reduced by 20 percent.

• Quality Threshold: The quality threshold determines the quality of the simplification. It can

be chosen between 0 and 1, but the higher the value the more fits the model to the reality.

0 means that all faces which are created or reordered are accepted.

• Preserve Boundary of the Mesh: The boundaries of the mesh are not destroyed by the

simplification.

• Preserve Normal: Important to preserve the original orientation of the surface as well as

to stop the flipping of the faces. Should be always ticked on. The only disadvantage is an

increase of the processing time.

• Preserve Topology: The QECD algorithm can change the topology of the mesh, so that this

is a very nice possibility to tick on this parameter.

• Optimal Position of Simplified Vertices: Each simplified vertex will be placed that the quadratic

error is minimized.


• Planar Simplification: It’s an additional simplification which can very good improve the sim-

plification quality of the planar portion of the mesh. It tries to preserve the shapes of the

triangles.

• Weighted Simplification: The vertex quality is used as a weighting factor. The weight is

used as a error amplification value, that means a vertex with a high quality value will not be

simplified, but vertices will low quality value will be strongly simplified.

• Post-Simplification Cleaning: After the QECD the mesh will be cleaned. The algorithm tries

to remove all the geometrical and topological inconsistencies (unreferenced vertices, small

holes, duplicated vertices, etc.).

After the remeshing the different parts of the class ice were merged by zippering (Fig. 5.5.4).

Figure 5.5.4: Zippering Process in Meshlab

With this tool meshes can be merged into a single one by using the Controlled Adaptive Mesh

Zippering method by Marras, Ganovelli and Cignoni (Marras et al., 2010), which is an extension

of the zippering algorithm of Turk and Levoy (1994). The new extended algorithm provides the

possibility to eliminate the data redundancy at the borders.


The whole model was remeshed again after the zippering with the QECD by 90 percent. After

that the smoothing of the surface follows with the Taubin smooth (Fig. 5.5.5), also named as

lambda-mu smooth. It is a fast, well known method for smoothing meshes that operates in two

passes. The first pass replaces each vertex with a weighted average of the positions of its nearby

points, which tends to contract the mesh. The second pass counteracts this contraction.

Figure 5.5.5: Smoothing in Meshlab

So Taubin is volume preserving, and since it operates locally, it’s also fast. Essentially the Taubin

algorithm corresponds roughly to Gaussian smoothing but with the difference of a scale factor �

which changes from iteration to iteration between a positive � and a negative value µ . For more

information look in Taubin et al. (1996). All in all the whole ice surface remains 1,622,443 triangles

which were exported to Autodesk R� 3D Studio Max as an .obj file format for the visualisation part

[see Chap. 5.2].


5.6 MODELLING OF THE GEOMETRIES OF THE ICE CAVE

The plug-in PointCloud for AutoCAD R� is designed to work with laser scan data. Unfortunately

AutoCAD R� only supports a few data formats for import. Principally AutoCAD R� is a powerful

software which can handle up to two billion points of scan data. The software PointCloud R�

from Kubit delivers the solution for the import of more import options of scan data directly from

the laser scanner. After the import of the original point clouds the geometric models can be

constructed semi-automatically. Therefore the point clouds had to convert in the .ptc format in

the kubit PointCloud file format or in the AutoCAD file format .pcg. Point clouds are characterized

by their size (number of points), their noise (n) and the average distance of points (a). Because

different modelling and fitting algorithms need these parameters for functioning correctly (Kubit

Inc., 2012). By clicking in the area of one step the software is able to detect the step existing of

points automatically (Fig. 5.6.1). So the edge area of the step has to adjust manually (Fig. 5.6.2.

Afterwards it is possible to copy this one modelled step to the next one in the point cloud whereby

the software again is able to detect the the next step existing of points automatically (Fig. 5.6.3).

This was done for each step section itself. By selecting all modelled steps the hight of the steps

were adjusted to 3 cm. This procedure was even made with the paths and the beams at the side

of the steps.

Figure 5.6.1: Clicking in the Area of Points to

Create Steps

Figure 5.6.2: Determine of the Edge Area in

the Point Cloud

Figure 5.6.3: Generating of the Steps by

Copying the Modelled Step


For the handrails, which already have the geometry like cylinders in the point cloud, the tool

Cylinder Fitting (Kubit Inc., 2012) was very helpful. By clicking in the cylindrical shapes of the

point cloud the software was able to detect the cylindrical shapes automatically (Fig. 5.6.4, Fig.

5.6.5). By clicking a second time the end of the cylinder was determined (Fig. 5.6.6). Afterwards

the software wants to know the diameter and the handrail was constructed in a very easily and

precision way7.

Figure 5.6.4: Clicking in the Area of Points to

Create Cylinder

Figure 5.6.5: Formed Cylinder from the Point

Cloud

Figure 5.6.6: Connection between Two Cylin-

ders

The network of the steps, paths and handrails presents a permanent model8 for further measure-

ments which could be integrated in future models very fast. All the constructed geometries were

exported as a "drawing" .dwg file format which is the native format in the CAD family to which

AutoCAD R� and Autodesk R� 3d Studio Max belonging to.

7More information in the appendix (B.5.1, B.5.2, B.5.3, B.5.4)8Appendix: The whole model of the steps and paths of the ice cave (B.5.5)


File Format Drawing (.dwg)

The drawing file format is a binary format which stores two and three- dimensional data with it’s

metadata. They are splitted in four parts: the head, the tables, the blocks and the geometrical

part. The head provides the definition of the working area, like dimensioning, adjustments for

curves or positions of local and global coordinate systems. The tables consists of information

about colours or stroke width, whereas the blocks contained information about elements which

are grouped into blocks to reduce memory consumption. The geometrical part saves the whole

geometry with it’s attributes like lines, polygons, curves or text with their colour, size or type. A lot

of different software packages, even ones which are not belonging to the Autodesk R� CAD family

are able to interact with this kind of data format, which shows the significance of this format in

this industry.


6 TEXTURIZATION OF THE CAVE

MODEL

The FARO R� Photon 120/20 laser scanner only provides intensity images. The option to use a

camera at the top of the laser scanner was not an acceptable possibility because of the darkness

and winding formations of the ice cave as well as due to energy consumption. Therefore the next

important processing step is the texturing of the cave model. The complex software package

Autodesk R� 3D Studio Max provides modules for three- dimensional modelling, animation and

rendering. Using the polygon modelling and texture mapping tools, it is possible to process the

individual parts of the ice cave.

6.1 TEXTURIZATION OF THE ICE

With the Material Editor Tool (Fig. 6.1.1), textures can be mapped onto objects by drag-and-drop,

and particular settings of the object properties let objects appear more realistic. Hence, the ice

got some roughness and a slight transparent gleam (Fig. 6.1.2). This can be adjusted with the

tool maps. Therefore a bitmap file can be loaded as a map in the material editor. For this case

a diffuse map, a normal ice texture image as a bitmap file, were used. This file was opened in

Adobe R� Photoshop R�. With the adjustment tool hue and saturation, the saturation was reduced,

that it looks like a greyscale image. These image was used in the material editor as the specular

map1. The same procedure was taken to receive a bump map with the difference to adjust the

levels2. For the appearance of the ice three different maps were used. By drag and drop the

texture was applied on the ice area3.

1Such a specular image is shown in the appendix (B.6.1)2Such a bump image is shown in the appendix (B.6.2)3The appendix shows one more texturized image of the ice area (B.6.3)

Chapter 6 Texturization of the Cave Model 53

Figure 6.1.1: Material Editor

with Settings to Create Ice

Texture

Figure 6.1.2: Mesh of the Texturized Ice

6.2 TEXTURIZATION OF THE ROCK

Likewise, the software provides a solution for the recurring texture of the rock. For this the tool

UVW Mapping is very helpful. Here it is possible to adjust direction and size of the mapped

texture to preclude visible repetitions. An object with an assigned two- dimensional material

texture (or a material that contains two- dimensional texture) must have mapping coordinates.

These coordinates specify how the map is projected onto the material, and whether it is projected

as a ”decal”, tiled or mirrored. Mapping coordinates that can be used to define how a texture map

is assigned to an object are also known as UV or UVW coordinates.

Unfortunately to texturize the whole three- dimensional model of the rock in the ice cave was to

costly with the background to texturize the rock for the whole model in a realistic way. A .bmp file

of the model was prepared and imported in Adobe R� Photoshop R�. The model was mapped with

photographs of the ice cave (Fig. 6.2.1). Each placement in the .bmp file of the size 8000 x 8000

of pixels, where the point of view of the scanner was, got its own photograph, that the cave will

look like the real one (Fig. 6.2.2).

After these steps the .bmp file was imported to Autodesk R� 3D Studio Max. Whereas the ice as

well as the steps and paths were texturized in Autodesk R� 3D Studio Max.


Figure 6.2.1: The Original Images of the Ice

Cave follow One Another

Figure 6.2.2: The Model of the Cave lies over

the Images like a Stencil

File Format bitmap image file (.bmp)

The bitmap image file format .bmp is a simple raster graphics image file format on Microsoft R�

Windows platforms. These formats are stored in a device-independent bitmap (DIB) format which

allows Windows to display the bitmap on any type of display device. The format is capable to store

2D digital images with height, width and resolution. Likewise it’s possible to store colour with their

depth and optionally with data compression, alpha channels and colour profiles. The image file

consists of a bitmap-file header (contains information type, size and layout of the file), a bitmap-

information header (contains information dimensions, compression type and the colour format),

a colour table (contains the colours in the bitmap) and an array of bytes (Reddy, 1994).


6.3 TEXTURIZATION OF THE STEPS, PATHS AND HANDRAILS

The texturization of the steps and the paths as well as the beams at the side of the stairs was a

time-consuming process. With the help of the portal http://www.cgtextures.com the textures

were downloaded as a bitmap image file. Even by drag and drop the texture file could mapped on

the geometry. In the modify panel of 3D Studio Max clicking on the modifier list the UVW Mapping

opens.

The steps and paths were created as planes and fitted into each geometry. With the tool gizmo

it was possible to rotate the texture on the geometry. In so doing, it was paid attention that every

step and path around got another texture to got it more realistic (Fig. 6.3.1). It was not possible

to draw up for each step or path it’s own texture. So after some steps the texture will repeat. All

in all 40 different textures were used for the steps and 15 different textures for the paths.

Figure 6.3.1: A Rendered Image of the Steps Figure 6.3.2: The Steps with the Handrails


http://www.cgtextures.com

The handrails were texturized like the ice area [comp. Chap. 6.1]. With the help of the Material

Editor a colour with a silver gleam was created and dragged on the cylindrical shapes (Fig. 6.3.2).

Therefore all these shapes were selected before.

In addition to texturing, the software provides the opportunity to generate animations, which will

be rendered afterwards. Therefore it is possible to define camera paths, e.g. with a target camera

which focuses on one object during the flight or with a free camera which has a single icon to

animate. The software can shade a scene’s geometry using light, material textures and environ-

mental settings for background and atmosphere. The rendering process might take some time

because of a huge amount of data.


7 GENERATING THE WHOLE

FLY-THROUGH THROUGH THE

ICE CAVE

7.1 CAMERA PATH

The virtual fly-through was generated by the software PointoolsTM

View 1.8 Pro. It is possible to

import single images of the cave model in the software package and to create a virtual fly-trough

through the series of approximately 23 000 .jpg images with approximately one billion of points

very easily and fast. Therefore camera positions, all in all 293, were required.

Figure 7.1.1: The User Interface of PointoolsTM

View 1.8 Pro

With this kind of software it was possible to create a virtual fly-through through the raw data, the

point clouds (Fig. 7.1.1). The whole fly takes 15 minutes. For the professional way of virtual flying

in this case, the opportunity to export the fast generated camera path as a .txt file and to import

it in the texturized geometry model in Autodesk R� 3D Studio Max was taken (Fig. 7.1.2).

Chapter 7 Generating the Whole Fly-Through through the Ice Cave 59

Figure 7.1.2: A View in the Texturized Ice Cave

File Format Joint Photograph Experts Group (.jpg)

In computing, JPEG is lossy compression technique for colour images and a commonly used

method for digital photography (image). The term is an acronym for Joint Photographic Experts

Group, which created the JPEG standard. The kind of compression can be adjusted for printing

(good quality), internet (acceptable quality) and poor quality. After each storage JPEG losses

image quality. It is possible to work with RGB or CMYK, to integrate ICC profiles and to store

layers and Lab values. No alpha channels are supported. Nevertheless it offers a good ratio

between quality and storage space.

File Format Text (.txt)

It is the simplest file format for MS-DOS and Windows use a common text file format. It uses a

form of ANSI, 0EM or Unicode (16 or 8-UTF) encoding and can be view with a normal text editor.

Because of their simplicity, text files are commonly used for storage of information. A simple text

file needs no additional metadata to assist the reader in interpretation, and therefore may contain

no data at all, which is a case of zero byte file.


7.2 CUT AND MUSIC

Autodesk R� 3D Studio Max offers the opportunity to render a video likewise consisting of single

images. These images were imported in Adobe R� After Effects R� (Fig. 7.2.1) and merged as a

composition, where the particular images were strung together.

Figure 7.2.1: The User Interface of Adobe R� After Effects R�

This composition which seems like a video was loaded in Adobe R� Premiere Pro R� (Fig. 7.2.2),

where different kinds of videos and video formats were rendered. These single compositions

can be loaded in Adobe R� Premiere Pro R� and can be merged together. In the same way the

opening credits and the credits were produced. The software Premiere Pro R� offers a lot of

different transitions between the different sequences which allows a fluent passage between the

particular video sequences.


Figure 7.2.2: The User Interface of Adobe R� Premiere Pro

The music "The ghost" supplied by the polish group Overcolored serves as background music for

the fly-through. This music were cut in the software Adobe R� Soundbooth R� (Fig. 7.2.3), because

it exists a lot of fly-through videos in a short and long version.

Figure 7.2.3: The User Interface of Adobe R� Soundbooth


8 CONCLUSION AND RESULT

It was proved that laser scanning point clouds can display the basis to create an extremely accu-

rate three- dimensional model of cavities of complex shape. The utilized software packages are

well suitable for the extraordinary amount of data. It has to be noted that the first data evaluation

of the whole ice-filled part of the cave including the mass of data needs an enormous amount

of time. By means of the generated model the ice surface of the Eisriesenwelt could be calcu-

lated with high accuracy. This ice surface calculation can be used as the basis and reference

for further change monitoring. With the generated model and further measurements of the ice

surface research can be focused on the flow conditions inside this dynamic cave and the impact

of the global warming to the sensitive ecosystem. The gangway as well as the majority of the rock

surface do not need to be created again for further scientific observations in the model. At the

same token, this project served the generation of a photo-realistic model of the natural monument

Eisriesenwelt. A first fly-through through the cave was generated and offers the opportunity to

explore the cave system in its hugeness (Milius and Petters, 2012).

Chapter 8 Conclusion and Result 64

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Botsch, M., Kobbelt, L., Pauly, M., Alliez, P., and Lévy, B. (2010). Polygon Mesh Processing. Ak

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Bibliography 68

APPENDIX

A IMPRESSIONS OF THE ERW

ii

A.1 IMPRESSIONS OF THE ERW

Figure A.1.1: Ice Sculptures in the ERW (Permission by Owner of the ERW)

Figure A.1.2: View from the "Ice Mammoth" to Hymirburg (Permission by Owner of the ERW)

Appendix A Impressions of the ERW iii

Figure A.1.3: View Inside the "Posselthalle" (Permission by Owner of the ERW)

Figure A.1.4: One Staff Member in the Hymirburg (Permission by Owner of the ERW)

Appendix A Impressions of the ERW iv

A.2 TABLE OF GEOLOGICAL PERIODS

Fa

me

nn

ian

Giv

etia

n

Eife

lia

n

Lo

ch

ko

via

n

Pra

gia

n

Fra

sn

ian

Em

sia

n

Lu

dfo

rdia

n

Go

rstia

n

Ho

me

ria

n

Shein

woodia

n

Te

lych

ian

Ae

ron

ian

Rh

ud

da

nia

n

P h a n e r o z o i c

P a l e o z o i c

Prid

oli

Lu

dlo

w

We

nlo

ck

Lla

ndovery

Up

pe

r

Mid

dle

Lo

we

r

Devonian

411.2

±2.8

416.0

±2.8

422.9

±2.5

428.2

±2.3

Lo

pin

gia

n

Gu

ad

alu

pia

n

Cis

ura

lia

n

Up

pe

r

Up

pe

r

Mid

dle

Mid

dle

Lo

we

r

Lo

we

r

Up

pe

r

Up

pe

r

Mid

dle

Mid

dle

Lo

we

r

Lo

we

r


P a l e o z o i cM e s o z o i c

CarboniferousPermianJurassic Triassic

150.8

±4.0

~ 1

55.6

161.2

±4.0

164.7

±4.0

167.7

±3.5

171.6

±3.0

175.6

±2.0

183.0

±1.5

189.6

±1.5

196.5

±1.0

199.6

±0.6

203.6

±1.5

216.5

±2.0

~ 2

28.7

237.0

±2.0

~ 2

45.9

~ 2

49.5

251.0

±0.4

318.1

±1.3

260.4

±0.7

253.8

±0.7

265.8

±0.7

268.0

±0.7

270.6

±0.7

275.6

±0.7

284.4

±0.7

294.6

±0.8

299.0

±0.8

303.4

±0.9

307.2

±1.0

311.7

±1.1

Tith

on

ian

Kim

merid

gia

n

Oxfo

rdia

n

Ca

llo

via

n

Ba

jocia

n

Ba

tho

nia

n

Aa

len

ian

To

arc

ian

Plie

nsbachia

n

Sin

em

uria

n

He

tta

ng

ian

Rh

ae

tia

n

No

ria

n

Ca

rnia

n

La

din

ian

An

isia

n

Ole

nekia

n

Ind

ua

n

Changhsin

gia

n

Wuchia

pin

gia

n

Ca

pita

nia

n

Wo

rdia

n

Ro

ad

ian

Ku

ng

uria

n

Art

inskia

n

Sa

km

aria

n

Asse

lia

n

Gzh

elia

n

Ka

sim

ovia

n

Mo

sco

via

n

Ba

sh

kiria

n

Serp

ukhovia

n

Vis

ea

n

To

urn

ais

ian

Penn-

sylvanian

Missis-

sippian

359.2

±2.5

345.3

±2.1

328.3

±1.6

374.5

±2.6

385.3

±2.6

391.8

±2.7

397.5

±2.7

407.0

±2.8

443.7

±1.5

436.0

±1.9

439.0

±1.8

418.7

±2.7

Ed

iaca

ran

Cry

og

en

ian

To

nia

n

Ste

nia

n

Ecta

sia

n

Ca

lym

mia

n

Sta

the

ria

n

Oro

siria

n

Rh

ya

cia

n

Sid

eria

n

Ne

o-

pro

tero

zo

ic

Ne

oa

rch

ea

n

Mesoarc

hean

Pale

oarc

hean

Me

so

-

pro

tero

zo

ic

Pa

leo

-

pro

tero

zo

ic

ArcheanProterozoic

P r e c a m b r i a n

~635

850

1000

1200

1400

1600

1800

2050

2300

2500

2800

3200

3600

4000

~4600

Up

pe

r

“Io

nia

n”

Ca

lab

ria

n

Se

lan

dia

n

To

rto

nia

n

Se

rra

va

llia

n

La

ng

hia

n

Bu

rdig

alia

n

Aq

uita

nia

n

Yp

resia

n

Ch

att

ian

Ru

pe

lia

n

Pria

bo

nia

n

Da

nia

n

Th

an

etia

n

Ba

rto

nia

n

Lu

tetia

n

Ca

mp

an

ian

Sa

nto

nia

n

Tu

ron

ian

Co

nia

cia

n

Alb

ian

Ap

tia

n

Be

rria

sia

n

Ba

rre

mia

n

Va

lan

gin

ian

Ha

ute

rivia

n

Ma

astr

ich

tia

n

Ce

no

ma

nia

n

Pia

ce

nzia

n

Me

ssin

ian

Za

ncle

an

Ge

lasia

n


C e n o z o i c M e s o z o i c

1.8

06

0.1

26

0.7

81

2.5

88

5.3

32

7.2

46

11.6

08

13.8

2

15.9

7

20.4

3

70.6

±0.6

65.5

±0.3

83.5

±0.7

85.8

±0.7

~ 8

8.6

93.6

±0.8

40.4

±0.2

37.2

±0.1

33.9

±0.1

28.4

±0.1

23.0

3

48.6

±0.2

55.8

±0.2

58.7

±0.2

~ 6

1.1

99.6

±0.9

112.0

±1.0

125.0

±1.0

130.0

±1.5

~ 1

33.9

140.2

±3.0

145.5

±4.0

CretaceousPaleogeneNeogene

SystemPeriod

EonothemEon

Erathem

Era

Stage

Age

Age

Ma

GSSP

Epoch

Series

EonothemEon

ErathemEra

System

Stage

Age

Age

Ma

GSSP

Epoch

Series

Period

EonothemEon

Erathem

Era

Age

Ma

GSSP

GSSA

SystemPeriod

EonothemEon

Era

Stage

Age

Age

Ma

GSSP

Epoch

Series

Period

3.6

00

Olig

oce

ne

Eo

ce

ne

Pa

leo

ce

ne

Plio

ce

ne

Ple

isto

ce

ne

Up

pe

r

Lo

we

r

0.0

117

INT

ER

NA

TIO

NA

L S

TR

AT

IGR

AP

HIC

CH

AR

TIn

tern

ational C

om

mis

sio

n o

n S

tratigra

phy

421.3

±2.6

426.2

±2.4

Tre

madocia

n

Da

rriw

ilia

n

Hirn

an

tia

n

Pa

ibia

n

Up

pe

r

Mid

dle

Lo

we

r

Se

rie

s 3

Sta

ge 3

Sta

ge 2

Fo

rtu

nia

n

Sta

ge 5

Dru

mia

n

Gu

zh

an

gia

n

Sta

ge 9

Sta

ge 1

0

Flo

ian

Da

pin

gia

n

Sa

nd

bia

n

Ka

tia

n

Sta

ge 4

Se

rie

s 2

Te

rre

ne

uvia

n

Fu

ron

gia

n

CambrianOrdovician

~ 5

03

~ 5

06.5

~ 5

10 *

~ 5

15 *

~ 5

21 *

~ 5

28 *

460.9

±1.6

471.8

±1.6

488.3

±1.7

~ 4

92 *

~ 4

96 *

~ 4

99

542.0

±1.0

455.8

±1.6

445.6

±1.5

468.1

±1.6

478.6

±1.7

Silurian

Erathem

System

542

359.2

±2.5

145.5

±4.0

Quaternary

ICS

Cop

yrig

ht ©

201

0 In

tern

atio

nal C

omm

issi

on o

n S

trat

igra

phy

Su

bd

ivis

ion

s o

f th

e g

lob

al g

eo

log

ic r

eco

rd a

re

form

ally d

efin

ed

by t

he

ir lo

we

r b

ou

nd

ary

. E

ach

un

it

of

the

Ph

an

ero

zo

ic (

~5

42

Ma

to

Pre

se

nt)

an

d t

he

ba

se

of

Ed

iaca

ran

are

de

fin

ed

by a

ba

sa

l G

lob

al

Bo

un

da

ry S

tra

toty

pe

Se

ctio

n a

nd

Po

int

(GS

SP

)

,

wh

ere

as P

reca

mb

ria

n u

nits a

re f

orm

ally s

ub

div

ide

d

by a

bso

lute

ag

e (

Glo

ba

l S

tan

da

rd S

tra

tig

rap

hic

Ag

e,

GS

SA

).

De

tails o

f e

ach

GS

SP

are

po

ste

d o

n t

he

ICS

w

eb

site

(w

ww

.str

atig

raph

y.or

g).

N

um

erica

l a

ge

s o

f th

e u

nit b

ou

nd

arie

s in

th

e

Ph

an

ero

zo

ic a

re s

ub

ject

to r

evis

ion

. S

om

e s

tag

es

with

in t

he

Ca

mb

ria

n w

ill b

e f

orm

ally n

am

ed

up

on

inte

rna

tio

na

l a

gre

em

en

t o

n t

he

ir G

SS

P lim

its.

Mo

st

su

b-S

erie

s b

ou

nd

arie

s (

e.g

., M

idd

le a

nd

Up

pe

r

Ap

tia

n)

are

no

t fo

rma

lly d

efin

ed

.

Co

lors

are

acco

rdin

g t

o t

he

Co

mm

issio

n f

or

the

Ge

olo

gic

al M

ap

of

the

Wo

rld

(w

ww

.cgm

w.o

rg).

Th

e lis

ted

nu

me

rica

l a

ge

s a

re f

rom

'A

Ge

olo

gic

Tim

e S

ca

le 2

00

4', b

y F

.M.

Gra

dste

in,

J.G

. O

gg

,

A.G

. S

mith

, e

t a

l. (

20

04

; C

am

brid

ge

Un

ive

rsity P

ress)

an

d “

Th

e C

on

cis

e G

eo

log

ic T

ime

Sca

le”

by J

.G.

Og

g,

G.

Og

g a

nd

F.M

. G

rad

ste

in (

20

08

).

Th

is c

ha

rt w

as d

raft

ed

by G

ab

i O

gg

. In

tra

Ca

mb

ria

n u

nit a

ge

s

with

* a

re in

form

al, a

nd

aw

aitin

g r

atifie

d d

efin

itio

ns.

Eo

arc

he

an

Had

ean

(info

rmal

)

Ho

loce

ne

Mio

ce

ne

Se

pt.

20

10

Figure A.2.1: Table of Geological Periods

Appendix A Impressions of the ERW v

A.3 ICE CAVE LOCATIONS DIFFICULT TO ACCESS

Figure A.3.1: Very nar-

row Way-Through compli-

cated the Laser Scanning

Process

Figure A.3.2: The more

Windings the more Scan-

ner Positions are neces-

sary

Figure A.3.3: The Ice It-

self entails a lot of Wind-

ing Formations with un-

reachable Places

Figure A.3.4: The Rock forms Itself overlaying Structures which can be seen only from One Side

that another Scanner Position have to put up

Appendix A Impressions of the ERW vi

A.4 ANALOGUE MAP

Figure A.4.1: The whole Map with all the 158 Locations of the TLS listed as Red Dots

Appendix A Impressions of the ERW vii

B WORKFLOW OF THE

PROCESSING STEPS

viii

B.1 WORKFLOW OF THE PROCESSING STEPS

Raw Data of FARO Laser Scanner .fls

FARO Scene 4.6.fls

Autodesk 3d Studio Max.max

AutoCAD.dwg

Polyworks.pwk

.fws FARO Workspace

.xyz file format

PlugIn PointCloud

Pointools.pod

Adobe After EffectsAdobe Soundbooth

.jpg

.xyz file format

.jpg file format

.mp4 file format

Saved

.ptc file format

.obj file format

Texture

.jpg file format

Geomagic.wrp

Point Cloud Flight Through

.obj file format

Meshlab.mlp

.obj file format

Adobe Photoshop.psd

.bmp file format

.obj file format

.dwg file format

Figure B.1.1: The Workflow of the Processing Steps

The blue boxes shows the different software packages and their own native file formats. The

arrow represents the step to the next software package with a compatible exchange format.

Appendix B Workflow of the Processing Steps ix

B.2 FARO R� SCENE 4.6

Figure B.2.1: Arranging of the Windows during the Registering Process

Figure B.2.2: Because of the Humidity in the Ice Cave the Mirror steamed up. This Scan had to

Deleted

Appendix B Workflow of the Processing Steps x

B.3 PROCESSING OF THE RAW POINT CLOUD DATA

Figure B.3.1: The whole Point Cloud Model of the Steps and Paths

Figure B.3.2: A Part of the Point Cloud Class Step, Path and Handrails. The Black Point on the

Steps is one Laser Scanner Position, where no Information is given

Appendix B Workflow of the Processing Steps xi

B.4 POLYWORKS R�

Figure B.4.1: The whole Model of the Point Cloud Class Rock

Figure B.4.2: It is a Transition from a Point Cloud to a Geometrical Model, which shows that

during the Meshing Process the Borders are taking over exactly

Appendix B Workflow of the Processing Steps xii

Figure B.4.3: The IMCompress Tool in PolyWorks R� with a Reduction of 30 Percent

Figure B.4.4: The Point Cloud Class Ice. The

Ice is Located on the Wall above Rock and

shows Small Structures

Figure B.4.5: The triangulated Model of the

Class Ice represents the Point Cloud Class

Ice exactly

Appendix B Workflow of the Processing Steps xiii

Figure B.4.6: The Holes under the Steps

have to be closed, which the Scanner couldn’t

reach

Figure B.4.7: The triangulated Model shows

that the Holes at the Steps were closed very

neat

Figure B.4.8: A Part of the Point Cloud Class

Ice at the "Ice Mammoth"

Figure B.4.9: The Path were closed. Wooden

Boards are lying over this Ice Surface

Figure B.4.10: During the Meshing and Triangulation the PC need a lot of Capacity

Appendix B Workflow of the Processing Steps xiv

B.5 PLUG IN POINTCLOUD FOR AUTOCAD R�

Figure B.5.1: A lot of different Bars in one

Scene

Figure B.5.2: Clicking in the Point Cloud to

build a Bar

Figure B.5.3: Winding Bars were necessary

to connect different Sections

Figure B.5.4: By Clicking the Two Cylinders a

rounded Winding Bar was created

Figure B.5.5: The whole Model of the Generated Steps and Paths

Appendix B Workflow of the Processing Steps xv

B.6 TEXTURIZATION

Figure B.6.1: The Saturation has to reduced to receive a Greyscale Image, a Specular Map

Figure B.6.2: The Black and White Slider have to adjust closer to get more Contrast, a Bump

Map

Appendix B Workflow of the Processing Steps xvi

Figure B.6.3: A Scene of the Texturized Ice with a Classical Mesh Smooth

Appendix B Workflow of the Processing Steps xvii

C DVD

xviii

C.1 DVD

This DVD contains the whole bat flight through the ice cave in a short and a long version as well

as this semester thesis as an Adobe R� PDF file.

Appendix C DVD xix

Documents

PROCESSING STEPS OF TLS FROM POINT CLOUD TO 3D MODEL