Photogrammetric software as an alternative to 3D laser ...826106/FULLTEXT01.pdf · DEGREE PROJECT,...

DEGREE PROJECT, IN , SECOND LEVELMEDIA TECHNOLOGY

STOCKHOLM, SWEDEN 2015

Photogrammetric software as analternative to 3D laser scanning in anamateur environment

MARKUS WARNE

KTH ROYAL INSTITUTE OF TECHNOLOGY

SCHOOL OF COMPUTER SCIENCE AND COMMUNICATION (CSC)

EXAMENSARBETE VID CSC, KTH

Fotogrammetrisk programvara som alternativ

till laser 3D-‐skanning i amatörmiljö

Photogrammetric software as an alternative to

3D laser scanning in an amateur environment

Markus Warne

[email protected]

Examensarbete i medieteknik

Handledare på CSC var Vasiliki Tsaknaki

Handledare på CUT var Krzysztof Skabek

Examinator: Haibo Li

Uppdragsgivare: Politechnika Krakowska (Cracow University of Technology)

Photogrammetric software

as an alternative to 3D laser

scanning in an amateur

environment

Abstract Photogrammetric software today is at a level where it is accessible to the mainstream

public and without larger effort is able to reconstruct digital 3D models from

photographic input. This thesis investigates the performance of photogrammetricly

reconstructed models and evaluates them by comparing the results to their

corresponding reconstructed models from a 3D laser scanner with a focus on smaller

objects in an amateur environment. The evaluation is performed on four different

objects, which are all individually compared to their scanned counterpart. They are

compared both with a subjective judgment of quality and by numerically measuring the

point-‐to-‐point distance on the models. From the results conclusions are drawn that the

methods can produce similar results albeit there are many performance factors

discovered for a good reconstructions with photogrammetry. The properties of the

physical object and the quality of the visual input data stand out as the most important

factors.

Fotogrammetrisk

programvara som alternativ

till laser 3D-‐skanning i

amatörmiljö

Abstrakt Den fotogrammetriska programvaran som existerar idag är tillgänglig för allmänheten

men framförallt kapabel att återskapa digitala 3D-‐modeller utan större ansträngning.

Denna rapport utforskar och utvärderar möjligheterna att återskapa dessa objekt för att

sedan jämföra hur dessa står sig gentemot motsvarande återskapade objekt med en 3D

laser skanner. Fokus ligger på att se hur mindre objekt kan återskapas i en amatörmiljö.

Testerna genomförs på fyra olika objekt genom att först återskapa dessa digitalt m.h.a.

fotogrammetri för att sedan jämföra dessa inviduellt med motsvarande modeller

återskapade m.h.a. 3D-‐skanning. Utvärderingen sker subjektivt med en bedömning av

kvalité men även genom att mäta avstånden från punk till punkt på modellerna. Från

resultaten kan slutsatserna dras att det går att nå likvärdiga resultat med fotogrammetri

som 3D-‐skanning men dessa beror på ett antal kritiska faktorer. Objektets fysiska

egenskaper samt kvalitén av den visuella data som används framstår som nyckelfaktorer

för att lyckas med en bra digitalt återskapad modell.

1 Introduction ................................................................................................................................................... 1

1.1 Goal of the thesis ................................................................................................................................. 2

1.2 Research questions ............................................................................................................................ 2

1.3 Limitations ............................................................................................................................................. 3

2 Background .................................................................................................................................................... 4

2.1 Related research ................................................................................................................................. 4

2.2 Triangulation ........................................................................................................................................ 5

2.3 3D laser scanning ................................................................................................................................ 5

2.3.1 How laser triangulation sensors work .............................................................................. 5

2.4 Photogrammetry ................................................................................................................................. 5

2.4.1 The basics of Photogrammetry ............................................................................................ 6

3 Method ............................................................................................................................................................. 8

3.1 Literature study ................................................................................................................................... 8

3.2 Quantitative evaluation .................................................................................................................... 8

3.3 Qualitative observations .................................................................................................................. 8

4 Evaluation setup .......................................................................................................................................... 9

4.1 Data generation -‐ Photogrammetric approach ...................................................................... 9

4.1.1 Photo environment setup and camera parameters .................................................... 9

4.1.2 Photogrammetric processing: Agisoft’s Photoscan .................................................. 10

4.1.2.1 Camera alignment .......................................................................................................... 10

4.1.2.2 Dense point cloud ........................................................................................................... 10

4.1.2.3 Mesh construction .......................................................................................................... 11

4.1.3 Photogrammetric processing: Autodesk’s 123D Catch .......................................... 11

4.2 Data generation -‐ laser scanning approach .......................................................................... 12

4.2.1 Environment and setup ........................................................................................................ 12

4.2.2 Konica Minolta Vivid 9i ......................................................................................................... 12

4.2.3 Model reconstruction from point cloud ........................................................................ 12

4.3 Data comparison .............................................................................................................................. 13

4.3.1 Alignment ................................................................................................................................... 13

4.3.2 Measurement ............................................................................................................................ 13

5 Results ........................................................................................................................................................... 15

5.1 Case 1 – Quadric object ................................................................................................................. 15

5.1.1 3D print reconstructed with PhotoScan ........................................................................ 16

5.1.2 3D print reconstructed with 123D Catch ...................................................................... 17

5.2 Case 2 – Angel figure ...................................................................................................................... 18

5.2.1 3D model reconstructed with PhotoScan ..................................................................... 19

5.2.2 3D model reconstructed with 123D Catch ................................................................... 20

5.3 Case 3 – Monkey figure ................................................................................................................. 21



5.4 Case 4 – Wooden cat ....................................................................................................................... 24



6 Analysis and discussion ......................................................................................................................... 27

6.1 Comparing the results of the photogrammetric reconstructions and the laser

scanned reconstructions .......................................................................................................................... 27

6.2 Strengths and weaknesses of the photogrammetric reconstruction software

applications ................................................................................................................................................... 28

6.3 The photogrammetric reconstruction process as a whole ............................................ 29

6.4 Can photogrammetry yield similar results to 3D laser scanning when used in an

amateur home setting for smaller objects? ..................................................................................... 30

7 Conclusion ................................................................................................................................................... 31

7.1 Future research ................................................................................................................................ 32

8 References ................................................................................................................................................... 33

9 Appendix ...................................................................................................................................................... 35

9.1 Case 1 – deviation results ............................................................................................................. 35

9.1.1 Photoscan model compared to 3D scanned model .................................................. 35

9.1.2 123D Catch model compared to 3D scanned model ................................................ 35









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1 Introduction Recently, there has been a growing interest in 3D printing technologies, with a number

of hardware and applications with a focus on the context of everyday use (Crum 2014).

This development indicates a future where 3D printing could be a tool for personal use

for everyone, not just experts. The 3D printing techniques are quickly advancing to

become more accessible to the general public with new models created specifically for

home use and a lower budget (Matter and Form 2014). What has not been discussed in

depth is the other side of the spectrum. What will we print? The models and data must

come from somewhere. As the demand and ability to print 3D models increases, the

supply of models must also follow according to basic economic theory. When this

happens, the market will desire a method for gathering 3D model data that is accessible

on an amateur scale to as many individuals as possible, at a low cost.

Methods such as laser scanning are not new, the first triangulation laser scanning

technology was developed already in 1978 (Mayer 1999). However, these methods were

and are still inaccessible to the mainstream public, at least to some degree, as the cost of

acquiring the technology or the knowledge needed to operate it is simply too high for

the average user. A relatively cheap alternative to laser scanning is stereo

photogrammetry. With the help of specific software and the advanced triangulation

algorithms that are available today, this method can be used, similarly to a 3D scanner,

to reconstruct and digitally model real life physical objects from just a set of ordinary

photos. These methods now open up new possibilities when it comes to creating and

sharing 3D models on a much larger scale, by amateur users, as they become more

accessible from an economic and technological viewpoint to the general public.

This is a very important area as these technologies are on the brink of becoming

mainstream and integrated to our everyday lives. 3D printing and 3D reconstruction

might be as common as sharing a file over the internet is today, in just a couple of years.

It is important to analyze these different methods, where they are in their current state

and possibly draw conclusions on what still needs to and can be improved in the future.

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1.1 Goal of the thesis The goal of this thesis is to take a closer look at 3D reconstruction of objects, by using

photogrammetry software as an alternative to laser scanning. Furthermore, to

investigate if this is a viable alternative for mainstream users and available to generate

models of a comparable quality to that of a reconstructed model from a laser scanner.

The main focus will be on comparing the models reconstructed from the two methods

mentioned above (reconstruction with laser scanning and reconstruction with stereo

photogrammetry), as described in Figure 1. Specifically, I will investigate the deviations

of the surfaces between the two methods while trying to assess the quality of the

models, by comparing them to each other numerically.

Figure 1 – Overview of reconstruction methods

1.2 Research questions • Can photogrammetry yield similar results to 3D laser scanning when used in an

amateur home setting for small objects?

o Numerical comparison of deviation between the reconstructed surfaces

• How do these two methods (laser scanning and stereo photogrammetry)

reconstructing models compare to each other?

o Strengths and weaknesses

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1.3 Limitations The comparison is limited to two photogrammetric methods and the laser scanning is

used just as reference measurement. The quality of the scanned models and accuaracy

for the specific scanner has already been carried out in another publication (Spytkowska

2008). Ideally a set of models from several laser scanners would be used as to be able to

generalize the results from laser scanning and identify similarities. In addition, only two

photogrammetric software applications will be used to reconstruct 3D models, which

could also limits the conclusions drawn from comparing the two techniques and

identifying general strengths and weaknesses.

Furthermore, the quality of the reconstructed objects will be assessed subjectively, due

to the nature of such qualitative evaluations. The limitation is the fact that there is no

digital original object to compare and quantify the deviation from the digital

reconstructions, as in most cases the originals are small physical objects.

The reconstructed models will be compared to a digital representation of the original, in

this case, a 3D scanned version of the original object, which in turn will be treated as

reference for deviation measurements. This will provide quantifiable results of deviation

between the two methods, 3D scanning and photogrammetry, but there is no way to

compare these to that of the original object.

One of the big limitations of this thesis is that in order to stay true to the “amateur

mainstream user” perspective a number of commercial software applications have been

used in parts of the process. This generates some “black box” parts where the

transparency of the process is limited, as we only know what we put in and what comes

out. Assumptions are made in these cases based upon general procedures and

algorithms within the field but complete certainty can’t be achieved. It has been made

clear which these parts are and when assumptions are made they are clearly indicated.

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2 Background 2.1 Related research Laser scanning has historically been used for scanning small objects in a controlled

environment, where there is a possibility of scanning the object in 360 degrees angle.

This is due to the fact that 3D scanners often have a small field of view and larger objects

often have to be scanned in several iterations and then combined together in order to

reproduce and complete the model (Chen et al. 2000; Seitz & Curless 2006). However

there are some exceptions with laser scanners such as LIDARs which are laser scanners

optimized for capturing large geographic areas and similar scenarios. On the other hand,

photogrammetry has mostly been used in these types of situations, when there is a need

to scan large-‐scale areas, such as air photography, cartography, mapping of

archaeological sites and other situations with large objects, when there is no need to

capture small details but rather focus on extracting measurements etc. Photogrammetry

is rapidly becoming more and more common, for reconstructing high detailed 3D

models (Blizard 2014; Poznanski 2014). This can be seen especially on the Internet,

where its accessibility to the mainstream public has inspired many hobbyists and so

called DIY (do it yourself) enthusiasts (Blizard 2014) but also in the entertainment

industry such as games (Poznanski 2014) and movies (Wolff 2004). Previously

photogrammetry has also been used in combination with laser scans as a compliment to

provide accurate textures for the model that were generated by laser scanning.

Attempts to reconstruct digital 3D models with the help of photogrammetry were

already done in 1984 (Benard 1984) and there are even some cases where the results of

the reconstructions have been compared to laser scanning techniques (Baltsavias 1999;

Fassi & Fregonese 2013). However most of them are, as mentioned earlier, focused on

archaeological, architectural or geo-‐data scenarios where the objects are usually very

large in scale. This thesis focuses on the perspective of smaller sized objects, up to one

cubic meter, and as some earlier research suggests (Baltsavias 1999) this area might be

more challenging for photogrammetric reconstruction of models. In addition, 3D

scanners have largely dominated the digital reconstruction of small objects as they are

usually built for this purpose whereas reconstruction with the help of photogrammetry

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has not been applied in these scenarios as often and if so only partly or complimentary

for smaller objects.

2.2 Triangulation Triangulation is a central concept for both of the techniques applied in this thesis, as

both photogrammetry and most laser scanning methods are based around this principle.

It is a method used to calculate the position of points in 3D space. It is used in a wide

range of applications and scenarios such as navigation, astronomy and many more due

to its broad and dynamic origin. Triangulation works by mathematically intersecting

converging lines in space from at least two known points to that of the investigated

point. By measuring the difference in the angle to the investigated point the precise

location of the point in 3D space can be determined. (Spytkowska 2008)

2.3 3D laser scanning 2.3.1 How laser triangulation sensors work

A triangulating laser scanner simplified consists of two components, a transmitter and a

receiver. The transmitter usually consists of a laser diode that projects a ray of light on

the object. A charged coupled device (CCD) sensor detects the reflection and due to

displacements in the object the angle of reflection varies depending on form and

distance. Thus the difference can be measured due to the principles of triangulation.

From this data, a point cloud is generated based on each specific measurement, each

point with a specific distance from the scanner. This measurement is carried out

thousands of times to generate a large point cloud representing the object. This discrete

point data cloud can then be interpolated, usually with the help of some complimentary

software, to create a 3D surface that consists of not just points but polygons.

(Spytkowska 2008; Dold & Brenner 2006)

2.4 Photogrammetry Photogrammetry is the science of acquiring measurements and 3D coordinates from

photographs. It can be compared to the reverse engineering of photographs as it aims to

turn 2D information into 3D information, the opposite of a camera (Figure 2).

6

Photogrammetry is often applied in topography scenarios like satellite and aerial

photography but also in close range scenarios, such as 3D reconstruction.

Figure 2 – Input and output of optical data capturing methods

2.4.1 The basics of Photogrammetry

The core principle for photogrammetry is triangulation. Due to several photographs (at

least two or more) with overlapping information, rays, as they are called, can be

calculated from the camera position to the object. By calculating the difference in angle

between the different rays the distance and position of the camera can be established.

This is also exactly the way our eyes work when estimating depth. In photogrammetry

the difference in the (x,y)-‐plane is used to triangulate the measured point. Furthermore

7

photogrammetry applies this principle to multiple points at a time with theoretically no

limit to the number of points measured at the same time (Slama et al. 1980).

To be able to compare the 3D laser scanning method to the photogrammetric approach

we have to go from physical 3D domain to digital 3D domain as seen in Figure 2.

However strictly photogrammetry consists of 2D input in the form of photos, thus we

have to include the photography part from Figure 2 to achieve the complete flow of

physical 3D domain to digital 3D domain.

As mentioned before (Figure 2) photogrammetry is in a way the reversed process of

photography. Unfortunately the photographic process is not perfect as information is

lost when taking a photo, if it was perfect, as in no information lost, just two photos

would be more than enough to recreate the 3D scene. So to compensate for this missing

information several photographs (absolute minimum of two) have to be used to aid the

calculation. The coordinates acquired from these calculations are the final result from

photogrammetry, this can be presented in the form of a point cloud or some other data

set that then usually is used further to extrapolate a 3D surface (Greve 1996; Schenk

2005).

For best results the images used as input for photogrammetry a few parameters are

important. First is the focus of the camera, since photogrammetry uses pixel for points in

triangulation blurry images are very tricky for pinpointing the position of features and

other elements that are out of focus. Furthermore the resolution is a very important

parameter much due to the same reason mentioned earlier meaning more pixels equal

more accurate calculations. Consistent lightning is also a factor that helps

photogrammetry in identifying various elements in the photograph. Varying light casts

different shadows from picture to picture this can have a destructive effect, as the same

feature can have varying intensity and color depending on the image analyzed at the

moment (Greve 1996; Schenk 2005).

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3 Method 3.1 Literature study To see what has been done in the field of 3D reconstruction with the use of

photogrammetry a literature study has been done. Google Scholar and KTH Royal

Institute of Technology’s “Primo” service have been used as primary knowledge wells

for academic research within the area. Furthermore high detail 3D reconstruction from

photogrammetry is a relatively new concept relevant information has been found on

blogs and other technical news themed websites. Google search and Wikipedia have

been used as a compliment or in conjunction to the named sources above.

3.2 Quantitative evaluation Quantitative methods are applied in this report to measure the difference in distance

between points on several reconstructed 3D surfaces. This is done with the help of

software that generates points on the reference surface and then tries to match them

with the test surface. Quantitative distance data is received for each of these points.

Unfortunately only the disparity between two reconstructed objects can be compared

quantitatively and not between the reconstructed objects and the original. This is due to

the fact that the original is not in the digital domain and any “conversion” from physical

to digital is simply another form of reconstruction method. This leaves the “quality”

parameter, from original to reconstruction, unquantifiable and a subjective matter. What

can be compared are the disparities of the surfaces from the reconstructed models,

which is also the main goal in this thesis, to find out if the methods are comparable and

yield similar results in a non professional environment.

3.3 Qualitative observations Furthermore the resulting reconstructed models will be assessed from a qualitative

approach with the aim to give some way of categorizing the perceived quality and

similarity of the digital reconstructed object compared to the physical original. This

assessment is purely subjective and may vary depending on the individual. This

observation is carried out with the purpose of another input into the main research

question, whether or not the tested methods yield comparable results. A method might

9

give relatively small quantifiable differences but the discrepancy in perceived quality

between the original object and the reconstructed model may be very large.

4 Evaluation setup The photogrammetric evaluation process consists of two major parts, the first is the

reconstruction of the physical object in 3D domain – this will be called data generation.

The second part consists of analyzing the reconstructed objects and comparing them to

each other. This is where the quantified data is extracted from the objects – this will be

called data comparison.

As the software applications used in this thesis are commercial, the specific algorithms

and techniques used are not available to the public domain. Therefore a general

approach to reconstruction with photogrammetry and laser scanning has been

described and for the specific software that follows all information that is available for

each application will be presented and assumptions based on what is known.

4.1 Data generation -‐ Photogrammetric approach 4.1.1 Photo environment setup and camera parameters The data generation environment was set up in a home setting with varying lightning

conditions as to stay in line with the thesis main goal of analyzing the possibility for

mainstream public to apply this technique in a non professional context. A tripod was

used to aid the stability and positioning of the camera and the lightning consisted of

mixed indoor light. The camera was placed as close as possible to the object for the

majority of the picture to cover most of the image area to preserve as much detail as

possible.

The camera used was a FUJIFILM X100S. The pictures taken had the aperture value of 16

as to maximize the depth of field for maximum amount of the element to be in focus.

Depending on the lightning registered by the camera different shutter speeds were

applied although most of the taken pictures had a shutter speed of ¼ seconds. The

pictures were saved digitally in .jpg format with a resolution of 4896 x 3264 pixels.

10

As mentioned before resolution and focus are two very important parameters for the

reconstruction process and these have been taken into consideration in this setup.

However consistent lightning is also an important factor for best results from

photogrammetric reconstruction but there has been no effort in this setup to minimize

this. This is to mimic home conditions of mainstream users to stay in line with the thesis

goal and as in a home environment there is usually no way to achieve evenly lit objects

such as with studio lightning conditions. However it is assumed that a good depth of

field and focus can be achieved in a home environment.

4.1.2 Photogrammetric processing: Agisoft’s Photoscan

The procedure of photograph processing and 3D model construction is described with

the following four stages according to the PhotoScan manual (Agisoft LLC 2011).

4.1.2.1 Camera alignment The software searches for common points in the collection of photographs. To be able to

match them, the software also calculates the position and orientation of the camera for

each picture. It is very probable that this is carried out with some form of triangulation

described earlier. The result of this process is a sparse point cloud where several points

have been identified and matched over the different camera positions. It is important to

have in mind that several points can be calculated and matched per camera location (a

single photo). However this sparse point cloud is not what the reconstruction of the 3D

model is based on unless explicitly specified by the user. The information that is used

further in the process of reconstruction is mainly the set of camera positions gathered in

this stage, presumably with the intention of using them as a starting point for further

triangulation of points.

4.1.2.2 Dense point cloud

The generation of a dense point cloud is done with the help of the estimated camera

positions and the respective matching photographs. Unfortunately no more information

is available on this stage. It is likely that a more complex triangulation process is used in

this stage to generate a dense point cloud with the help of the initially calculated camera

positions.

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4.1.2.3 Mesh construction The third stage consists of constructing a 3D polygonal mesh representing the object

surface based on the dense point cloud.

In most cases two algorithmic methods are available that the software can apply for 3D

mesh generation:

• Height field – Optimized for modeling of planar surfaces, such as terrains or bas-‐

reliefs. For aerial photography processing, it requires a lower amount of memory

and allows for larger data sets processing.

• Arbitrary -‐ For closed objects, such as statues, buildings, etc. It doesn't make any

assumptions on the type of the object modeled, which comes at a cost of higher

memory consumption.

In this thesis the arbitrary algorithm method was used for all cases of photogrammetric

reconstruction processing.

Once the mesh is constructed, the user has the ability to somewhat edit it. Non-‐complex

corrections such as mesh decimation (simplification), removal of detached components

and the closing of holes can automatically be performed by the software in the mesh

generation process.

Furthermore the final stage of the model construction is the application of textures, as

this is not relevant in this thesis it will be ignored.

4.1.3 Photogrammetric processing: Autodesk’s 123D Catch Unfortunately very little information can be found about 123D Catch’s reconstruction

process. Presumably the process is somewhat similar to that of PhotoScan. It can also be

assumed that again triangulation is a key algorithm and that it is based on characteristic

points identified in the set of pictures.

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The practical process is very similar to PhotoScan’s. The user takes a set of overlapping

photos of the object from different angles. The difference is the processing is done via

the software applications server. The user uploads the photoset to the server and after a

while, depending on image size and quantity the server returns a reconstructed 3D

model.

This is both an advantage and a flaw comparing it to PhotoScan. By automating the

process only the photographs have to be provided, this can be helpful in the aspect of a

mainstream user and it can be assumed that the server has better computational power

than if the reconstruction would be performed locally. However this limits the amount of

influence the user has on the reconstruction such as key parameters for reconstruction

and insight into the process and therefore also the end result.

4.2 Data generation -‐ laser scanning approach 4.2.1 Environment and setup The scans were carried out in Gliwice, Poland at the Institute of Theoretical and Applied

Informatics (part of the Polish Academy of Sciences) in one of their offices. The laser

scanner was a Konica Minolta Vivid 9i and it was connected to a mechanically rotatable

platform, which together with the scanner were operated through the complimentary

native software for the scanner.

For the scanner to encompass the whole object several scans had to be carried out from

different angles, this was controlled with the rotatable platform. The angle of rotation

per scan was 30° up to a complete circle of 360° resulting in 12 overlapping scans.

4.2.2 Konica Minolta Vivid 9i The scanner used for the conducted tests is a Konica Minolta Vivid 9i. The Vivid 9i is a

scanner created for small to medium sized objects which it can capture with high detail.

This is a triangulating laser scanner with a charged coupled device (CCD) receiver like

the ones described in chapter 2.3.1.

4.2.3 Model reconstruction from point cloud

When the laser scanner has completed its registration of points on the object, each of the

point clouds are interpolated individually to create a part of the objects surface. Theses

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pieces have to be aggregated into a single surface to complete the process. This was

done by aligning each individual piece in respect to the others and merging them into a

single shape to construct a 3D model. The software application used for this was

Geomagic Qualify which is used to measure, align and compare fabricated parts to their

digital blueprint in the production industry.

4.3 Data comparison 4.3.1 Alignment

Alignment is a very important part of the measurement process, as the objects need to

be identically aligned for the distance results to be accurate. This is in turn a problem as

the objects vary in shape and form, it becomes a subjective judgement if the objects are

aligned properly. First n-‐point alignment is used for a rough alignment of the objects.

This is based on a physical person identifying common points on both objects and

marking these. The minimum for this alignment is 3 points but can be as many as one

would like, therefore the name n-‐point alignment. When the points are identified the

objects are matched up so the points on both objects align with eachother.

After this rough alignment a global registration algorithm is used. Randomly selected

points on the reference surface are used to reposition the test object to minimize the

overall distance measured from the points. This somewhat eliminates the subjective

factor from the alignment process but the first alignment step is still based on human

perception.

4.3.2 Measurement The laser-‐scanned surface is the reference surface for our comparison. The other

surfaces reconstructed with the help of the photogrammetric applications are the test

surfaces. For each comparison an amount of randomly selected points on the reference

surface, presumably depending on the number of vertices, are used to measure the

distance to the corresponding point on the test surface for each individual point with the

help of Geomagic Qualify. This in turn generates a data set of point pairs with their

measured distance that is used as the data for the quantifiable comparison of the

methods.

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Due to the surfaces being different in form and quality it is not always possible to map a

point on the reference surface to a corresponding location on the test surface. In these

cases the difference can’t be measured between the two points. However it is fair to

assume that the deviation in the current point in this case is larger or similar to the

maximum deviation of the measured points.

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5 Results In this section there are four cases presented with varying objects in complexity and size

to represent a wide spectrum of smaller objects that may be found in a home

environment for amateur 3D reconstruction. For each case a view of the original and all

reconstructions is shown with a description of each surface. Furthermore color maps

are presented from four different perspectives of the deviation between the compared

surfaces. The deviation is always measured in millimeters and the legend of the color

map shows that green areas are the ones closest to zero in deviation. Further towards

red are larger positive deviations and towards blue larger negative deviations.

5.1 Case 1 – Quadric object This first case is a type of calibration case different to the other test cases that follow in

this section. The original object here is a digital 3D model of a simple quadric surface

(A). The digital original was then reproduced into the physical domain (scale 1:1) with

the help of a 3D printer (B) to then again get reconstructed to digital domain (C&D) with

the help of the photogrammetric methods presented earlier. The physical object is small

and measures 100x95x45 mm (height x width x depth).

The interesting thing here is that we have a digital original giving us some way of

comparing the deviation between the original and the reconstructed models. This is not

possible in the other cases, as the original object is physical.

Figure 3 -‐ A) Digital original 3D model. B) Physical 3D-‐print based on A. C) Reconstructed

digital model with PhotoScan based on B. D) Reconstructed digital model with 123D Catch based

on B.

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5.1.1 3D print reconstructed with PhotoScan

What is presented here are four different perspectives (top, bottom, isometric and side

view), of the deviation between the original (A) and the reconstruction with the help of

PhotoScan (C).

Figure 4 – Case 1, 3D scan compared to reconstruction with PhotoScan

The numerical difference between the digital original and the reconstructed model is

very small. As shown in the full table of points in appendix chapter 9.1.1, about 96% of

the points are placed in the interval of -‐0,1386 to 0,304 mm. So the numerical difference

over 96% of the surface is less than half of a millimeter.

Deviation (mm) Max. Upper Deviation

Max. Lower Deviation

Average Deviation

Standard Deviation

1.1312 -1.0498 0.1594 / -0.0785 0.1

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5.1.2 3D print reconstructed with 123D Catch

The same perspectives as in the previous comparison are also displayed here. This is the

comparison of the digital original (A) and the reconstructed model with the help of 123D

Catch (D).

Figure 5 – Case 1, 3D scan compared to reconstruction with 123D Catch

The results are similar to the reconstruction with PhotoScan (C) and again the numerical

difference here is small, although a bit larger than the results from PhotoScan. What we

can see here is also that the differences gravitate a bit more towards a negative distance

difference whereas when reconstructing with PhotoScan we see the differences

gravitating towards a positive difference.



Average Deviation

Standard Deviation

0.845 -2.395 0.1825 / -0.2531 0.2282

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5.2 Case 2 – Angel figure In this case the original object is a physical figure of an angel, which consists of a mix of

small soft and hard features with an overall complex surface. The object is small and

measures 80x50x40 mm (height x width x depth). The comparison is conducted

between the photogrammetricly reconstructed models (C&D) from the two different

methods and compared to that of a reconstructed digital model from a 3D scanner (B).

Figure 6 -‐ A) Physical original. B) Digital 3D-‐model reconstructed with the Vivid 9i based on A. C) Reconstructed digital model with PhotoScan based on A. D) Reconstructed digital model with 123D Catch based on A

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5.2.1 3D model reconstructed with PhotoScan

Here we see four different perspectives (front, left, back and right), of the deviation

between the 3D-‐scanned reconstruction (B) and the reconstruction with the help of

PhotoScan (C).

The color map does not classify the grey areas in this comparison. This is due to a too

large discrepancy between the two compared surfaces in the specific area and the

deviation measurement algorithm cannot identify the corresponding point on the other

surface. Simply put a point on surface C does not match any point on surface B and

therefore the difference cannot be measured.

Figure 7 -‐ Case 2, 3D scan compared to reconstructed model with PhotoScan



Average Deviation

Standard Deviation

8.5393 -9.6250 0.6051 / -0.6442 1.0395

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The reconstructed model is very noisy and distorted, even so we see that the numerical

difference over most areas of the surface is still small, only 0.5 mm. With that said it is

important to acknowledge the grey areas where points were unable to match we can

assume the difference is greater than +/-‐5 mm.

5.2.2 3D model reconstructed with 123D Catch The same perspectives (front, left, back and right), of the deviation between the 3D-‐

scanned reconstruction (B) and the reconstruction with the help of 123D Catch (D). As

previously the color map does not classify the grey areas due to the same reasons in

5.2.1.

Figure 8 -‐ Case 2, 3D scan compared to reconstructed model with 123D Catch



Average Deviation

Standard Deviation

3.7093 -3.6926 0.6848 / -0.4766 0.8400

21

Although both photogrammetric reconstructions contain artifacts and some noise the

key thing to notice here is that there are larger areas on the test surface (D) which can’t

be matched to the reference surface (B) than in the previous comparison, C vs B. This

has a significant impact on the key values like average deviation as pieces of the dataset

are missing.

5.3 Case 3 – Monkey figure As in the previous case the original object here is a physical figure of three monkeys,

which consist of mostly small sharp features. The overall surface of the object is very

complex with a high amount of detail. The object measures 70x105x50 mm (height x

width x depth). A comparison is performed between the photogrammetricly

reconstructed models from the two different methods (C&D) and compared to that of a

reconstructed digital model from a 3D scanner (B).

Figure 9 -‐ A) Physical original object. B) Digital 3D-‐model reconstructed with the Vivid 9i based on A, C) Reconstructed digital model with PhotoScan based on A. D) Reconstructed digital model with 123D Catch based on A.

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5.3.1 3D model reconstructed with PhotoScan Four perspectives (front, left, back and right), of the deviation between the 3D-‐scanned

reconstruction (B) and the reconstruction with the help of PhotoScan (C) are shown

here.

Figure 10 – Case 3, 3D scan compared to reconstructed model with PhotoScan

This statue has a similar size comparable to Case 2 yet the discrepancies here are

significantly smaller. The deviation is also very evenly spread over the whole object

suggesting there is little to no noise.



Average Deviation

Standard Deviation

3.6367 -3.0904 0.1769 / -0.1312 0.1854

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5.3.2 3D model reconstructed with 123D Catch

The same perspectives (front, left, back and right). The deviation here is larger than in

the comparison of B vs C and the deviation is not as evenly spread out as in 5.3.1.

Figure 11 – Case 3, 3D scan compared to reconstructed model with 123D Catch



Average Deviation

Standard Deviation

6.1359 -7.7362 0.8444 / -1.3935 1.4147

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5.4 Case 4 – Wooden cat This case is unique in the sense that the object here is significantly larger than the

previous cases. The object measures 410x150x50 mm (height x width x depth). As

before in the previous cases the original object here is a physical figure of cat carved in

wood that consists mostly of large soft features and is quite simplistic in shape. The

comparison is conducted between the photogrammetricly reconstructed models from

the two different methods (C&D) and compared to that of a reconstructed digital model

from a 3D scanner (B).

Figure 12 -‐ A) Physical original 3D model. B) Digital 3D-‐model reconstructed with the Vivid 9i

based on A. C) Reconstructed digital model with PhotoScan based on A. D) Reconstructed digital

model with 123D Catch based on A.

5.4.1 3D model reconstructed with PhotoScan

We can see from the color map that a lot of noise has distorted the model quite heavily

in some areas yet still most of the surface only has a small deviation of about 1mm which

may seem like a lot compared to the earlier cases but taking into account the size of the

object it is actually a very small deviation.

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Figure 13 – Case 4, 3D scan compared to reconstructed model with PhotoScan



Average Deviation

Standard Deviation

20.3581 -20.3864 2.0123 / -1.7814 3.2267

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5.4.2 3D model reconstructed with 123D Catch

This reconstructed model is not as noisy as the previous case and the discrepancies are

much more evenly grouped. Again even though some areas are quite deviant most of the

surface has a very small deviation of 0.5 mm.

Figure 14 – Case 4, 3D scan compared to reconstructed model with 123D Catch



Average Deviation

Standard Deviation

10.6311 -7.8892 0.6068 / -0.9564 1.1290

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6 Analysis and discussion These comparisons are carried out between the 3D laser scan of the object and the

photogrammetric reconstruction so small numerical differences do not always

correspond to an ideal reconstruction as errors might very well exist in the laser scan of

the object (Spytkowska 2008). This is not a point that has or will be explored further in

this thesis, as the goal is only to compare the methods with each other and not with the

physical original on a quantifiable level. As explained earlier this is not possible in most

cases and neither the goal of this thesis. Nevertheless it is important to keep that in mind

when analyzing these results. We cannot assume the digital laser scan of the object is

ideal but it is the reference for comparison of the photogrammetric reconstruction

methods.

6.1 Comparing the results of the photogrammetric

reconstructions and the laser scanned reconstructions Although the numerical differences in all these cases are by comparison relatively small

excluding some minor areas it is important to note the difference in perceived quality of

the objects. Looking at the results, the conclusion that lower average deviation also

correlates to a better-‐perceived quality can be said to be true. Furthermore we can see

that the more evenly the deviation is spread out over the surface the better the

perceived quality of the reconstruction. Even if large areas are quite off, the key factor

affecting the perceived quality is noise, and large areas with similar deviation often

suggest small amounts of noise or at least a uniform distortion. This of course assumes

that the 3D laser scanner has reconstructed the physical original in a satisfying manner,

which is assumed here as discussed earlier, since it is these two surfaces we are

comparing and not the original. These conclusions were expected but cannot be said to

always be true since this is a subjective judgment and the perception of quality might

vary for each individual.

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6.2 Strengths and weaknesses of the photogrammetric

reconstruction software applications It is also interesting to try and analyze the strengths and weaknesses of the algorithms

of the two photogrammetric software applications in their ability to reconstruct the

surface of the object. In general we can see that the algorithms struggle when faced with

areas and whole surfaces that lack large or multiple changes, in other words smooth soft

features and same colored surfaces as seen in case 2 and 4. The type of change whether

it is surface based or color based seems to be of less importance. As we can see in case 1,

the simple and smooth surface of the quadric is compensated by the complex pattern

painted on the surface. Furthermore surface differences like the fur of the monkeys in

case 3 also result in the same type of change such as color changes due to lightning

giving the features a shadow and thus a different intensity. Therefore changes in either

surface or color result in a similar difference on a photograph and it can be assumed that

this is what the algorithms use to identify points of measurement for the triangulation as

mentioned in chapter 2. This property will be named Δ (Delta); objects with a high

amount of Δ produce better results when reconstructed.

The cat, case 4, is a good example where we see quite small Δ, as changes in color and

surface over large areas contribute to confusion for the photogrammetric applications as

they most likely have no way of identifying the points on the surface and therefore

struggle aggregating the orientation of the respective images.

We can also come to the conclusion here that Δ is not the only factor for good

photogrammetric reconstructions but also the density of the change. If we look at case 2,

the angel figure has some areas with soft round features (low Δ) and some areas like the

hair with high Δ. The areas with high Δ have been reconstructed quite well however the

lack of surface nearby with high amounts of Δ create a challenge for the reconstruction

algorithm and the result is quite poor. The conclusion that can be drawn from this is that

even if an object has a high amount of Δ it has to be spread out over the whole surface

for a good reconstruction, concentrated amounts will only give a good result in that

specific area. The density of Δ will be called ρ (rho).

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It is also clear that there is a difference in the algorithms between the two applications.

PhotoScan’s reconstruction generates a substantial amount of noise but still manages a

quite small overall numerical deviation over the whole surface, suggesting a more

intense frequency of points triangulated or a large difference in the interpolation

between points. In the case of 123D’s reconstruction the points calculated are probably

fewer, this contributes to, in most cases, that the perceived quality is much higher due to

the lower amount of noise and it still captures the overall features. However it’s

important to note that in the best reconstruction, case 3, PhotoScans point frequency

generates a more accurate reconstruction by capturing far more detail than 123D Catch

thanks to the larger amount of points calculated, although as mentioned above, this is

not always an advantage as errors can become plentiful.

Another interesting difference between the algorithms is that PhotoScans deviation is

usually positive, differences between the reconstructed surface point outwards from the

object however 123Ds deviation is often negative, pointing inwards from the surface.

This results in PhotoScans reconstructions generally ending up larger in size than their

equivalent laser scan and 123D reconstruction ending up smaller than their counterpart.

This is presumably a side effect of each applications calculation algorithm and it is hard

to know if it is related in any way to the perceived quality of the object although it can

have some impact when trying to recreate objects made to scale.

6.3 The photogrammetric reconstruction process as a whole Photogrammetry, as can probably derived from just the name, is highly dependent on

the input data, in other words the photographs quality. This is not the same subjective

qualities of the human perception of a “nice” picture, but rather good quality as in

maximum preservation of unaltered visual information as described in chapter 2.4.1 and

4.4.1. This is the main bottleneck for this technology and as photographs with altered or

low amount of visual information also give bad results when processed by the

reconstruction algorithm. Bad input can be somewhat mitigated with the help of high

amounts of Δ and ρ on the object but only to some extent. This thesis focus was more on

the results of photogrammetric reconstruction and comparing those to that of

reconstruction by 3D laser scanning. Even though the results are fairly impressing in

some cases, not only numerically but especially in perceived quality, such as case 3, it is

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fair to say that more work and time should have been focused on the process of

generating the input data, as this generates the biggest differences in quality in the

reconstruction process. Unfortunately this is very time consuming and somewhat

beyond the goal of this thesis as we focus on the whole process rather than the details of

each part, but nevertheless this is the most important and the most obvious conclusion

that can be drawn from these cases.

The dependency on input data goes so far that even lack of data is better than low

quality data. What this means is that photographs with low quality visual information

such as a blurry or heavily shadowed photographs do more harm than good. This is due

to the need to aggregate information from several photographs during the

reconstruction algorithm. Visual information of poor quality can confuse the algorithm

during the aggregation process. In the attempt to identify expected features or rather

points, based on assumptions from earlier presumably proper visual information, they

cant be recognized in the following picture as they differ in some form, even though it is

to the human eye the same point. This in the worst-‐case scenario leads to earlier proper

visual information getting discarded and also becoming useless.

Photogrammetry is all about good input data, and as mentioned before – if a photograph

was an ideal medium for information and no information was lost in the transformation

from three dimensions into two, the reconstruction process for photogrammetry

wouldn’t need more than a single photograph – unfortunately this is not the case.

6.4 Can photogrammetry yield similar results to 3D laser

scanning when used in an amateur home setting for

smaller objects? The short and simple answer to this question is yes. In case three with the monkey

statues we see a near ideal reconstruction, not only in numerical terms but also in

perceived visual quality of the object. However there are three main factors along the

way that have a significant impact on the results of the reconstruction. The quality of the

visual information preserved in the photograph, focus and lightning are two big

components in maximizing captured visual data. Secondly, the features of the object

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either form, color and intensity – Δ and the density of these changes – ρ, enabling the

reconstruction algorithm to establish a clear and robust set of points to use for

triangulation calculations. The third and final piece is the algorithm used for

reconstruction and calculation of the points, the transformation from two-‐dimensional

coordinates into three-‐dimensional ones. This is also where some questions are still

unanswered as the algorithms of these commercial software applications are not public

domain and have not been investigated in depth in this thesis. These components need

to be, not just average, but of good quality for the reconstruction to yield similar results

to that of a good 3D laser scanner.

7 Conclusion This thesis has described the background and general technology behind both methods,

3D scanning with laser and photogrammetry, as means of reproducing physical objects

in digital three-‐dimensional space. The focus has been to compare if, with the help of

photogrammetry software in a non-‐professional setting, it is possible to get results of

similar quality to that of a laser scanning approach which has been the preferred

method for some time when reconstructing smaller objects. This has been tested with

four cases of objects of varying surface features and size, these four cases are not enough

to draw generalized conclusions but still give a good spread of data as to see where

these methods strengths and weaknesses lie and what parameters affect this type of

reconstruction.

The results from the cases indicate that it is indeed possible with the help of

photogrammetry to reconstruct a model of very similar quality to that of one

reconstructed with a laser scanner. However it is also obvious that these good results

are not consistent and need not only good external conditions but also rely on the shape

and features of the objects surface. Objects with unique non-‐repeatable variations are

the ones that produce the best results when reconstructed. It does not seem to matter

whether these variations are physical, color or intensity based, the key result is

differentiated points or pixels on the resulting photograph that provide the algorithm

with highly varying points used to identify and match these in the following

photographs.

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It is fair to say that amateur photogrammetry can yield very good results when used for

digital reconstruction of 3D objects although as mentioned before these results of good

quality are not very consistent and require a certain amount of variables. If these

prerequisites are not matched the results in most cases are very underwhelming

compared to those of a laser scanner. However strictly numerically the results are not

very far off and it is debatable if the methods produce comparable results but when we

weigh in the perceived quality of the object the results are usually underwhelming in an

amateur setting.

7.1 Future research Three-‐dimensional reconstruction from photogrammetry is still relatively new

compared to other reconstruction techniques and it is interesting to see what can be

done to improve these methods in the future, as there is certainly potential for these

methods. For further research on this topic it would be interesting to look at

experiments with more focus on optimizing the process of photography, as this is the

core of photogrammetric reconstruction – the input data.

Another interesting angle of approach would be to compare amateur laser scanning with

the results of amateur photogrammetry. As in this thesis the focus has been on amateur

photogrammetry results compared to professional laser scanning it might be an unfair

comparison of the two techniques. Furthermore there seems to be a drive to produce

more and more equipment for 3D reconstruction on an amateur level as demand for

both 3D printers and 3D scanners rise.

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8 References

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Baltsavias, E., 1999. A comparision between photogrammetry and laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 54, pp.83–94.

Benard, M., 1984. Automatic stereophotogrammetry: a method based on feature detection and dynamic programming. Photogrammetria, 39, pp.169–181. Available at: http://www.sciencedirect.com/science/article/pii/0031866384900164 [Accessed December 5, 2014].

Blizard, B., 2014. The Art of Photogrammetry: Introduction to Software and Hardware. Available at: http://www.tested.com/art/makers/460057-‐tested-‐dark-‐art-‐photogrammetry/ [Accessed March 2, 2015].

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Fassi, F. & Fregonese, L., 2013. BETWEEN LASER SCANNING AND AUTOMATED 3D MODELLING TECHNIQUES TO RECONSTRUCT COMPLEX AND EXTENSIVE CULTURAL HERITAGE. ISPRS 3DArch, Trento, …, XL(February), pp.25–26. Available at: http://www.researchgate.net/publication/235742814_COMPARISON_BETWEEN_LASER_SCANNING_AND_AUTOMATED_3D_MODELLING_TECHNIQUES_TO_RECONSTRUCT_COMPLEX_AND_EXTENSIVE_CULTURAL_HERITAGE_AREAS/file/d912f51307c8faac65.pdf [Accessed March 19, 2014].

Greve, C., 1996. Digital photogrammetry : an addendum to the manual of photogrammetry, Bethesda : American Society for Photogrammetry and Remote Sensing.

Matter and Form, 2014. The Matter and Form 3D scanner. Available at: https://matterandform.net/scanner [Accessed March 2, 2015].

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Mayer, R., 1999. Scientific Canadian: Invention and Innovation From Canada’s National Research Council, Vancouver: Raincoast Books.

Poznanski, A., 2014. VISUAL REVOLUTION OF THE VANISHING OF ETHAN CARTER. Available at: http://www.theastronauts.com/2014/03/visual-‐revolution-‐vanishing-‐ethan-‐carter/ [Accessed March 2, 2015].

Schenk, T., 2005. Introduction to Photogrammetry. Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, pp.79–95. Available at: http://gscphoto.ceegs.ohio-‐state.edu/courses/GeodSci410/docs/GS410_02.pdf.

Seitz, S. & Curless, B., 2006. A comparison and evaluation of multi-‐view stereo reconstruction algorithms. Computer vision and …. Available at: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1640800 [Accessed May 12, 2014].

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9 Appendix 9.1 Case 1 – deviation results 9.1.1 Photoscan model compared to 3D scanned model

9.1.2 123D Catch model compared to 3D scanned model

Deviation interval (mm) # Points % amount -‐0.8782 to -‐0.4990 1616 1.4827 -‐0.4990 to -‐0.1198 66074 60.6217 -‐0.1198 to 0.1198 23582 21.6361 0.1198 to 0.4990 16476 15.1164 0.4990 to 0.8782 1245 1.1423

Deviation interval (mm) # Points % amount -‐1.1312 to -‐0.9658 7 0.0027 -‐0.9658 to -‐0.8003 8 0.0031 -‐0.8003 to -‐0.6349 6 0.0024 -‐0.6349 to -‐0.4695 45 0.0176 -‐0.4695 to -‐0.304 526 0.2061 -‐0.304 to -‐0.1386 3268 1.2804 -‐0.1386 to 0.1386 100728 39.4664 0.1386 to 0.304 144364 56.5634 0.304 to 0.4695 5774 2.2623 0.4695 to 0.6349 448 0.1755 0.6349 to 0.8003 47 0.0184 0.8003 to 0.9658 1 0.0004 0.9658 to 1.1312 2 0.0008

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9.2 Case 2 – deviation results 9.2.1 Photoscan model compared to 3D scanned model



Deviation interval (mm) # Points % amount -‐4.2042 to -‐3.4084 25 0.0602 -‐3.4084 to -‐2.6127 41 0.0987 -‐2.6127 to -‐1.8169 89 0.2143 -‐1.8169 to -‐1.0211 1310 3.1544 -‐1.0211 to -‐0.2253 9811 23.6245 -‐0.2253 to 0.2253 11712 28.2020 0.2253 to 1.0211 13912 33.4995 1.0211 to 1.8169 2411 5.8056 1.8169 to 2.6127 1205 2.9016 2.6127 to 3.4084 769 1.8517 3.4084 to 4.2042 244 0.5875

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9.3 Case 3 – deviation results 9.3.1 Photoscan model compared to 3D scanned model Deviation interval (mm) # Points % amount -‐3.6367 to -‐3.0609 1 0.0006 -‐3.0609 to -‐2.4851 2 0.0012 -‐2.4851 to -‐1.9093 0 0.0000 -‐1.9093 to-‐1.3334 1 0.0006 -‐1.3334 to -‐0.7576 3 0.0018 -‐0.7576 to -‐0.1818 16122 9.4107 -‐0.1818 to 0.1818 107687 62.8591 0.1818 to 0.7576 47352 27.6403 0.7576 to 1.3334 143 0.0835 1.3334 to 1.9093 2 0.0012 1.9093 to 3.0609 0 0.0000 3.0609 to 3.6367 2 0.0012


Deviation interval (mm) # Points % amount -‐7.7362 to -‐6.5113 2 0.0023 -‐6.5113 to-‐5.2864 0 0.0000 -‐5.2864 to -‐4.0615 2653 2.9958 -‐4.0615 to -‐2.8366 6492 7.3308 -‐2.8366 to -‐1.6117 12105 13.6690 -‐1.6117 to -‐0.3868 37165 41.9668 -‐0.3868 to 0.3868 15998 18.0650 0.3868 to 1.6117 10853 12.2552 1.6117 to 2.8366 2943 3.3232 2.8366 to 4.0615 294 0.3320 4.0615 to 5.2864 51 0.0576 5.2864 to 6.5113 2 0.0023 6.5113 to 7.7362 0 0.0000

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9.4 Case 4 – deviation results 9.4.1 Photoscan model compared to 3D scanned model



Deviation interval (mm) # Points % amount -‐8.9478 to -‐7.2646 5 0.0074 -‐7.2646 to -‐5.5813 35 0.0518 -‐5.5813 to -‐3.8981 233 0.3450 -‐3.8981 to -‐2.2148 4101 6.0715 -‐2.2148 to -‐0.5316 19616 29.0414 -‐0.5316 to 0.5316 33827 50.0807 0.5316 to 2.2148 8675 12.8433 2.2148 to 3.8981 872 1.2910 3.8981 to 5.5813 137 0.2028 5.5813 to 7.2646 29 0.0429 7.2646 to 8.9478 10 0.0148 8.9478 to 10.6311 4 0.0059

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