Precision Assessment of Artec Space Spider 3D Handheld

Scanner for Quantifying Tufa Formation Dynamics on Small

Limestone Plates (PLs)

Ivan Marić

, Ante Šiljeg

and Fran Domazetović

University of Zadar, Department of geography, Trg kneza Višeslava 9, 23 000, Zadar, Croatia

Keywords: Artec Space Spider, Precision Assessment, Tufa Formation Dynamics.

Abstract: In this paper precision assessment of the 3D handheld scanner Artec Space Spider was evaluated and critically

observed. Namely, a new application of handheld 3D scanners has been found in quantifying tufa formation

dynamic (TFD). Such measurements should be characterized by a high level of data quality because tufa

usually grows only a few millimeters annually. Therefore, a small limestone plate (PL) used as a substrate on

which TFD will be studied was scanned five times by two independent observers. Interval scans of PL were

processed using Artec Studio 15 Professional. Data processing consisted of five steps. The precision

assessment was determined by statistical analysis of derived sections and colored distance map (CDM).

Results showed that Artec Space Spider generates reliable results considering the characteristics of the

scanned object and it certainly can be used for TFD analysis. Also, results suggested that the application of

Artec Space Spider in the quantification of TFD can be regarded as a better approach in the context of

measurement reliability compared to other direct and indirect methods. The subject of future research will be

the precision and accuracy assessment of various 3D handheld scanners in scanning tufa formed in different

temporal resolutions with various surface complexity.

1 INTRODUCTION

Artec Space Spider is a non-contact active 3D

handheld scanner produced by Artec 3D. This 3D

scanner emits a pattern of structured light (SL) to the

observed object or scene and measures its

deformation (Abdel, 2011). It enables robust high-

quality capture of 3D geometry and can be actively

used throughout several fields (Eiríksson et al., 2016,

pp, 1). Data in this type of scanner is acquired in

relation to the internal coordinate system. Therefore

the position of the scanner must be determined using

specific reference features on the scanned surface

(Abdel, 2011).

Artec Space Spider is an ultra-high-resolution 3D

scanner intended for precisely capturing small objects

and complex details with accuracy up to 0.05 mm

(Artec 3D, 2020) and high resolution (Table 1). Due

to its superior precision, long-term repeatability in

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https://orcid.org/0000-0001-6332-174X

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data capture, and automatic temperature stabilization

it has been regarded as an excellent solution for

metrology applications (Motley, 2020). It combines

structured light 3D scanning (blue LED) with an

image-based approach (Reichert et al., 2016).

Table 1: Specifications of the Artec Space Spider.

Specifications

Artec Space Spider

3D point accuracy

0.05 mm

3D resolution

0.1 mm

3D accuracy over distance

0.05 + 0.3 mm/m

Working distance

0.2 – 0.3 m

Volume capture zone

2 000 cm³

Angular field of view (H*W)

30 × 21°

Texture

Yes

Texture resolution

1.3 mp

3D reconstruction rate

7.5 fps

Data acquistion speed

1 mln points/s

3D light source

Blue LED

2D light source

White 6 LED

Mari

c, I., Šiljeg, A. and Domazetovi

c, F.

Precision Assessment of Artec Space Spider 3D Handheld Scanner for Quantifying Tufa Formation Dynamics on Small Limestone Plates (PLs).

DOI: 10.5220/0010886900003185

In Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2022), pages 67-74

ISBN: 978-989-758-571-5; ISSN: 2184-500X

Datasets collected using the 3D handheld

scanners, intended for specific applications, should

meet certain specifications and provide the officially

stated data quality standards (Georgopoulos et al.,

2010, Abdel, 2011). Data quality represents the

ability of a given dataset to satisfy the set objective

(Campbell, Shin 2011). However, there is no general

agreement on which set of dimensions defines the

quality of data (Batini et al 2009, pp 6.). This term

generally covers two primary attributes of accuracy

and precision. It should be noted that not enough

focus has been placed on precision analysis and

quantitative accuracy of SL systems (Eiríksson et al.,

2016, Campanelli et al., 2016).

Precision, or “repeatability” of measurement, can

be defined as the internal accuracy of a device that is

determined through repeated measurements under

equal conditions (Hofer et al., 2005). Accuracy,

which has several definitions (Batini et al 2009, pp 7),

can be defined as the “nearness” of measurement to

an actual or “real” value (Campbell, Shin 2011).

Analyzing the data quality of the 3D handheld (SL)

scanner is a challenging task, which has only seen few

published standards and guidelines (Eiríksson et al.,

2016). Sometimes, data quality specifications given

by device producers should be taken with caution

because these values vary from instrument to

instrument and depend on the user's expertise and

individual calibration (Abdel, 2011). Furthermore, it

is not uncommon that in the official pamphlets of

specific devices measured precision (formal error) is

presented as accuracy (Santos et al., 2000).

Due to the high-resolution digital capture of

scenes or objects at submillimeter levels of accuracy,

and reduced time of data acquisition, Artec Space

Spider and similar scanners have been used in a wide

range of scientific fields and other activities from

reverse engineering processes and product design

(Allegra et al., 2016, Artec 3D, 2020), healthcare

industry (Allegra et al., 2016, Koban et al. 2016,

Modabber et al., 2016, Seminati et al., 2017, Verhulst

et al., 2018, Dessery, Pallari, 2018, Ritschl et al.,

2019, Özsoy et al., 2019, Varga et al., 2019, Artec 3D,

2020, Winkler, Gkantidis, 2020), forensics

(Sivanandan, Liscio, 2017, Buck et al., 2018, Zhang

et al., 2020), video game industry (Artec 3D, 2020),

soil erosion process (Wang et al., 2019), heritage and

cultural preservation (Allegra et al., 2016, Artec 3D,

2020), etc. Recently, a new potential application of

handheld 3D scanners has been found in quantifying

tufa formation dynamic (TFD) (Marić et al., 2020) on

small limestone plates (PLs). However, this research

requires the performance of interval PL scanning

within a specific local coordinate system (LCS) in

order to quantify and analyze spatio-temporal

changes at the specific location of the PL surface

(Marić, et al., 2021). Such measurements should be

characterized by a high level of data quality because

tufa usually grows and erodes, in most cases, only a

few millimeters a year (Marić et al., 2020). Therefore,

the main objective of this paper was to assess the

precision of the Artec Space Spider 3D handheld

scanner in quantifying TFD on small limestone PLs.

The analysis is conducted on an artificial limestone

plate (PL) that lacks tufa formation on its surface.

This was done because (1) in most studies similar

substrates (specific PLs) are used; (2) if you want to

study TFD using a 3D scanner, then it is necessary to

scan an empty PL because it represents the initial

(reference) model; (3) these type of object (without

formed tufa), represents the most difficult case for

scanning because they have very small dimension and

lacks distinctive features and color. Therefore, we

wanted to test the worst possible scenario, in which,

in one year of studying TFD only a tenth of a

millimeter of tufa growth occurs, or tufa growth does

not occur at all.

The precision assessment was done using the two

approaches; (a) colored distance maps and (b) defined

metrics from multiple (n=5) sections of the PLs.

The scope of this research can be interesting to the

researchers who use similar 3D handheld scanners to

model small objects whose properties make them

difficult to scan. However, the results of the paper are

primarily intended for the scientists whose primary

scientific interest is TFD and are considering using

3D handheld scanners and similar PLs design for

monitoring the tufa growth and erosion.

The paper was organized in the following

chapters. First, the Introduction describes in detail the

handheld 3D scanner and its applications. The

possible application in the studying of TFD is

mentioned. In order to determine its applicability in

this type of research, it was decided to determine its

precision given that the 3D point accuracy is stated in

the official specifications. The Material and Method

chapter describes in detail the data processing

workflow which consisted of several steps. In the

Results and discussion, the derived results of

precision from two used approaches are presented.

Then, in the Conclusion, the main findings of this

research were highlighted, and the consideration for

future research is given.

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

2 MATERIALS AND METHODS

The research methodology consisted of several steps

that included: (1) interval (n=5) scanning of the

selected PL before its immersion in the tufa-forming

watercourse; (2) processing of the acquired scans,

which consisted of several sub-steps that were unified

for all interval scans; and (3) a precision assessment

using two stated approaches (Figure 1).

Figure 1: Simplified methodological framework.

2.1 Scanning Procedure

In order to assess the precision of the Artec Space

Spider interval scanning of a specific limestone PL

(area ≈ 25 cm²) was conducted. This type of object is

the most challenging for scanning when optical 3D

scanners are used. Namely, 3D scanners rely on an

object’s unique geometry and in this case PL lacks

distinctive characteristics such as texture, features,

and color. However, this type of PL is often used in

TFD studies.

Scanning was done in an environment of constant

light exposure. The same PL was five times scanned

with Artec Space Spider by two independent

observers, each on two separate occasions. On the

surface of the PL there was no sedimented tufa

because the data quality assessment needs to be

determined on the smallest scanned surface within the

TFD research (initial model). This refers to the first

measurement, i.e reference state of the PL before it

was mounted in tufa forming watercourses. PL was

set on the created local coordinate system (LCS) and

then measured interval, five times with Artec Space

Spider. The LCS was used because the 3D scanner

axis (X, Y, Z) is relative to the scene being scanned.

Therefore, the LCS eases the adjustment of the 3D

model position onto one of the coordinate planes.

Each scanning took around 10-15 seconds. Distance

adjustment indicator in the Artec Studio 15

Professional was used to determine the ideal scan

distance. True localization was adjusted by either

moving 3D handheld scanners farther away or closer

to get the best possible quality of the scan. The

scanning was performed at a speed of around 6-7

frames per second (fps).

2.2 Data Processing

Scans of interval measured PL were processed using

Artec Studio 15 Professional, an industry-acclaimed

software package for advanced 3D scanning and data

processing. Geometry + Texture scanning mode was

used. Segmentation of the scans did not occur during

scanning. Data processing consisted of five steps.

First, (1) crop surrounding, with rectangular selection

tool was used, to delete unnecessary scanned area

around the PL. Then, using the (2) global registration

(GR) option frame positions across all scans were

optimized preparing them for further processing. It

converts all one-frame surfaces to a single coordinate

system using the information on the mutual position

of each surface pair (URL 1). Prior to the conduction

of the GR, user-defined parameters (key frame ratio -

KFR and feature search radius - FSR) were changed.

KFR was set on 1 and FSR was set on 3 mm. KFR

determines how many surfaces are treated as key

frames. A higher value than 0.7 can significantly slow

down the algorithm. The value of FSR was slightly

lowered in relation to the default value because a

small object with similar features was scanned. In

each scanning frame registration quality was

improved after GR (Table 2). After the GR, the

maximum error values decreased to a good

classification result (URL1).

Table 2: Maximum error values

for interval measurements

after the GR.

Scan ID

Artec Space Spider

Before GR

After GR

0.1

0.2

0.1

0.2

0.1

0.2

0.1

Error is the parameter that reflects frame registration quality. For

scans, it shows the maximum value among all the frames. The

larger the value, the less accurate the alignment (URL 1).

After the GR, large outliers and some noise were

deleted with (3) outlier removal tool. Outliers can

introduce additional errors in the model or produce

unwanted fragments. This approach for every surface

point calculates the mean distances between it and a

certain number of neighboring points, as well as the

standard deviation of these distances. If the mean

distances are greater than an interval defined by the

global-distances mean and standard deviation then

the point is recognized as an outlier and deleted. A

Precision Assessment of Artec Space Spider 3D Handheld Scanner for Quantifying Tufa Formation Dynamics on Small Limestone Plates

(PLs)

unique model surface was created using a (4) sharp

fusion tool. This creates a polygonal 3D model by

solidifying the processed captured and frames. Mode

sharp fusion was used because it perfectly

reconstructs fine features. Option simplify mesh was

not used because it decreases the number of polygons.

Then (5) texture was projected from the individual

frames onto the fused mesh. Finally (6), each derived

mesh model (n=5) was aligned according to the

created LCS by using the rigid align option and four

markers placed at the same location within LCS.

2.3 Precision Assessment

2.3.1 Section Analysis

Precision assessment of Artec Space Spider was

calculated using two approaches following examples

in literature (Patel et al., 2015, Reichert et al., 2016,

Varga et al., 2019, Seminati et al., 2017, Ozsoy et al.,

2019).

In the first approach, from derived models (n=5)

specific metrics were calculated from multiple (n=5)

sections placed at an interval of 1.5 mm across the PL

height (Z-axis). This is one of the measurement tools

included in Artec Studio. A section is the plane that

splits the model or scans into parts. Once the sections

are created, the panel displays its geometrical data.

The following metrics were used: MeshVolume

(MV), MeshArea (MA), Area (M), and Perimeter (P)

(Figure 2). These metrics Artec Studio automatically

generates and assigns them the listed acronyms.

These names have not changed. MA is called

MeshArea but it represents the area of the outer edge

of a plate. M (Area) is actually, the surface area of the

plate that includes the top and the outer edges (not the

bottom).

Figure 2: Schematic representation of derived metrics.

First, the average value of defined metrics for

each section was calculated which represented the

most probable (reference) value. Especially, total MV

(sum of MV sections) was calculated for each

measurement. Then, the absolute deviation (AD) and

percent deviation (PD) of the remaining metrics for

each section were derived. Finally, the precision of

Artec Space Spider is expressed as mean absolute

percent deviation (MAPD) for each defined metric.

Furthermore, relative standard deviation (RSD) as a

measure of precision, which quantifies the average

dispersion of a set of observations from an estimate

of the set’s mean value, was calculated. The RSD tells

whether the “regular” SD is small or large when

compared to the mean for the data set.

2.3.2 Colored Distance Maps (CDM)

In the second approach, colored or surfaces distance

maps of interval models were created. These maps

help to determine the variability in the shape and

volume of the surfaces (URL 2). This approach is

often used in quality control where comparison of the

original model with the scanned one is necessary. In

total 20 colored distance maps were made where each

model served as a reference one. For example, first,

model_1 was regarded as reference (model1-model2),

then model_2 was regarded as reference (model2-

model). This process was conducted for a comparison

of five interval models. The Artec Studio calculated

mean absolute distance (MADi), mean absolute

deviation (MADe), mean signed distance (MSD), and

root mean square (RMS) for each comparison. The

RMS error is a frequently used statistic metric in

evaluating the degree of inaccuracy (Campbell, Shin

2011). It shows the variation between the compared

3D surface. Lower RMS value indicates a similar

shape (higher precision), while the higher values

indicate higher disparity (lower precision) in 3D

models (Ozsoy et al., 2019). However, the calculated

RMS error from Artec Studio matches the STDEV

function in Excel. MADi is the average of absolute

deviation values (all deviations become positive).

MSD is an average value of all deviations (+ and -).

MADe represents the excel AVEDEV function which

returns the average of the deviations value from the

mean for a given set of data.

3 RESULTS AND DISSCUSSION

3.1 Section Analysis

Table 3 shows the total MV (mmᶟ) of PL for each

measurement and derived AD and PD. The reference

(mean) MV of scanned PL was 18012.2 mmᶟ. MAPD

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

was 0.387 %. The highest AD and PD were recorded

in the fourth measurement and amounted to 88.71

mmᶟ or 0.493%.

Table 3: Total MV for measurements and derived mean AD

and PD.

Measurement

(s1+s2+s3+s4+s5)

Total MV

(mmᶟ)

AD (mmᶟ)

PD (%)

17942.8

69.45

0.3856

17986.2

26.00

0.1443

18097.6

85.38

0.4740

18100.9

88.71

0.4925

17933.6

78.64

0.4366

MEAN

18012.2

69.64

0.3866

This value is acceptable because an empty PL was

scanned which represents the worst-case scenario in

TFD studies, one in which no tufa formed. However,

when precipitation occurs, the PL surface would have

a more recognizable shape, texture, and color making

it easier to scan and register. Also, the MV would be

bigger which would potentially reduce the PD.

Precision assessment of various scanners at scanning

tufa formed in different intervals (3, 12 months) and

with various surface complexity (plant fragments,

macroinvertebrates) will be the subject of future

research. Table 4 shows MAPD derived the

remaining metrics.

Table 4: MAPD for MA, M, and P.

Section

PD for MA

PD for M

PD for P

Plane1

0.105

0.088

0.040

Plane2

0.119

0.115

0.049

Plane3

0.134

0.103

0.032

Plane4

0.149

0.084

0.036

Plane5

0.176

0.055

0.068

Plane1

0.014

0.097

0.045

Plane2

0.019

0.129

0.049

Plane3

0.024

0.133

0.051

Plane4

0.032

0.141

0.076

Plane5

0.059

0.090

0.035

Plane1

0.109

0.088

0.043

Plane2

0.121

0.045

0.022

Plane3

0.134

0.037

0.018

Plane4

0.145

0.024

0.012

Plane5

0.182

0.064

0.031

Plane1

0.217

0.004

0.019

Plane2

0.233

0.037

0.015

Plane3

0.254

0.037

0.017

Plane4

0.280

0.044

0.002

Plane5

0.321

0.121

0.038

Plane1

0.206

0.276

0.107

Plane2

0.216

0.235

0.084

Plane3

0.230

0.235

0.067

Plane4

0.244

0.087

Plane5

0.268

0.329

0.019

MAPD

0.160

0.114

0.043

As expected, Space Spider in is more unreliable when

generating MV compared to other derived metrics

(Table 5).

Table 5: RSD derived for defined metrics.

Section

M1_5

Plane1

2.101

0.168

0.159

0.065

Plane2

2.087

0.181

0.149

0.057

Plane3

2.078

0.196

0.147

0.049

Plane4

2.075

0.213

0.149

0.062

Plane5

3.312

0.246

0.186

0.063

mean RSD

2.331

0.201

0.158

0.059

3.2 CDM Results

Green values on the following Figures (3, 4, and 5)

show areas with zero error, red shows error above the

reference surface, and blue below. The average RMS

error for all compared CDMs was 0.030 mm. Average

MADi was 0.0328 mm and average MADe was

0.0248 mm. All derived statistic metrics had a smaller

value of declared manufactured accuracy value. No

specific imprecision patterns were observed in the

respective CDMs.

Figure 3: Derived colored distance map (1).

Precision Assessment of Artec Space Spider 3D Handheld Scanner for Quantifying Tufa Formation Dynamics on Small Limestone Plates

(PLs)

Figure 4: Derived colored distance map (2).

Tufa growth and erosion rates can be expressed as the

height/thickness (mm a

-1

), volume (mm

-1

), and

mass (g a

-1

) formed at some period of time

(Gradzinski, 2010). In general, when considering the

annual tufa growth rate expressed through height, it is

around a few mm a

-1

, depending on a wide range of

parameters (Vázquez-Urbez et al., 2010, Marić et al.,

2020). So, if the TFD is expressed through a

Figure 5: Derived colored distance map (3).

volumetric approach, on a PL surface of 25 cm², when

4 mm of tufa is formed, the MV of tufa would be 10

000 mmᶟ. Since the determined highest AD for MV

is 88.71 (mmᶟ) this would result in an unreliable

calculation of the tufa MV by 0.8871%.

If the TFD is analyzed using the height (mm a

-1

)

approach, and since all metrics in CDM had a smaller

value (around 0.035 mm) of declared manufactured

accuracy, then this would result in an unreliable

calculation of the tufa height by 0.875 %. However, it

should be noted that the reliability of the

measurement can be better after the formation of tufa

occurs. It might seem contradictory but there is a lot

of complexity when it comes to scanning this type of

object (small, empty PL). In the next interval

measurements, after the volume of the scanned PL

increases, ie the tufa occurs on the PL surface, the

scanning process should be easier.

4 CONCLUSION

The aim of this study was to assess the precision of

Artec Space Spider in scanning the small limestone

PLs used for monitoring TFDs.

Although Space Spider is a high-quality 3D

scanner, its application in TFD measurement may be

limited due to the fact that they do not have a fixed

LCS which makes interval comparison of 3D models

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

easier and due to the fact that limestone PLs used for

monitoring TFD does not necessary have distinctive

characteristics (surfaces features). Therefore, the

first, initial scanning of a PL which does not have

formed tufa on its surfaces, can be very difficult.

The first drawback was solved by adding markers,

i.e creating the LCS for model alignment. The results

of the precision assessment showed that Space Spider

generates reliable results considering the

characteristics of the scanned object (empty PL) and

it certainly can be used in TFD analysis. Data

obtained with a Space Spider has high reproducibility

and reliability.

In future research, the comparison of the tufa

growth and erosion results obtained using hand-held

3D scanners and other indirect and direct methods

(Marić, et al., 2020), that can express growth and

eruption rates through volume (mm

-1

) and height

(mm a

-1

), will be done.

ACKNOWLEDGEMENTS

This work has been supported by Croatian Science

Foundation under the project UIP-2017-05-2694.

REFERENCES

Abdel, M. (2011). 3D Laser Scanners: History, Applications,

And Future. Civil Engineering Department, Faculty of

Engineering, Assiut University, Egypt,

http://iqvolution.ws/storage/app/public/uploads/files/

Inovation-2/Publikacii/3D-Laser-Scanners-History-

Applications-And-Future.pdf, 22 January, 2021.

Allegra, D., Gallo, G., Inzerillo, L., Lombardo, M., Milotta,

F. L. M., Santagati, C., Stanco, F. (2016, October). Low

Cost Handheld 3D Scanning for Architectural Elements

Acquisition. In STAG: Smart Tools and Apps in

computer Graphics, 3-4 October, 2016, Eds. Pintore, G.

and Stanco, F., Genova, Italy, (pp. 127-131).

Artec 3D (2020). Professional 3D scanning solutions, Artec

Studio, https://www.artec3d.cn/files/pdf/Artec3D-

Scanners-Brochure.pdf, 20 January, 2021.

Batini, C., Cappiello, C., Francalanci, C., Maurino, A.

(2009). Methodologies for data quality assessment and

improvement. ACM computing surveys (CSUR), 41(3),

1-52.

Buck, U., Buße, K., Campana, L., Schyma, C. (2018).

Validation and evaluation of measuring methods for the

3D documentation of external injuries in the field of

forensic medicine. International journal of legal

medicine, 132(2), 551-561.

Campanelli, V., Howell, S. M., Hull, M. L. (2016).

Accuracy evaluation of a lower-cost and four higher-

cost laser scanners. Journal of biomechanics, 49(1),

127-131.

Campbell, J. E., Shin, M. (2011). Essentials of geographic

information systems, Textbooks. 2, Liberty University,

https://digitalcommons.liberty.edu/cgi/viewcontent.cgi

?article=1001&context=textbooks, 21 January, 2021.

Dessery, Y., Pallari, J. (2018). Measurements agreement

between low-cost and high-level handheld 3D scanners

to scan the knee for designing a 3D printed knee brace.

PloS one, 13(1), e0190585.

Eiríksson, E. R., Wilm, J., Pedersen, D. B., Aanæs, H.

(2016). Precision and accuracy parameters in structured

light 3-D scanning. The International Archives of

Photogrammetry, Remote Sensing and Spatial

Information Sciences, Volume XL-5/W8, 7-15.

Georgopoulos, A., Ioannidis, C., Valanis, A. (2010).

Assessing the performance of a structured light scanner.

International Archives of Photogrammetry, Remote

Sensing and Spatial Information Sciences, 38(Part 5),

251-255.

Gradziński, M. (2010). Factors controlling growth of

modern tufa: results of a field experiment. Geological

Society, London, Special Publications, 336(1), 143-

191.

Hofer, M., Strauß, G., Koulechov, K., Dietz, A. (2005,

May). Definition of accuracy and precision—

evaluating CAS-systems. In International Congress

Series, Elsevier, Volume 1281, pp. 548-552.

Koban, K., Schenck, T. L., Giunta, E. R. (2016, November).

Using mobile 3D scanning systems for objective

evaluation of form, volume, and symmetry in plastic

surgery: intraoperative scanning and lymphedema

assessment. In Proceedings of the 7th International

Conference on 3D Body Scanning Technologies,

Lugano, Switzerland, 30 November - 1 December, 2016.

Marić, I., Šiljeg, A., Cukrov, N., Roland, V., Domazetović,

F. (2020). How fast does tufa grow? Very high ‐

resolution measurement of the tufa growth rate on

artificial substrates by the development of a contactless

image ‐ based modelling device. Earth Surface

Processes and Landforms, 45(10), 2331-2349.

Marić, I., Šiljeg, A., Domazetović, F., Cukrov, N. (2021).

A framework for using handheld 3D surface scanners

in quantifying the volumetric tufa growth. In

GEOMORPHOMETRY 2020, CNR Edizioni, Eds. M.

Alvioli, I. Marchesini, L. Melelli, P. Guth, Perugia,

Italy, 22-26 June 2020, 18.

Modabber, A., Peters, F., Kniha, K., Goloborodko, E.,

Ghassemi, A., Lethaus, B., ... & Möhlhenrich, S. C.

(2016). Evaluation of the accuracy of a mobile and a

stationary system for three-dimensional facial

scanning. Journal of Cranio-Maxillofacial Surgery,

44(10), 1719-1724.

Motley, P. (2020). Accuracy Testing of the Artec Space

Spider When 3D Scanning Tricky Objects,

GEOMESURE3D BLOCK https://gomeasure3d.com/

blog/testing-accuracy-artec-space-spider-when-3d-

scanning-tricky-objects/, 20 January, 2021.

Özsoy, U., Sekerci, R., Hizay, A., Yildirim, Y., & Uysal, H.

(2019). Assessment of reproducibility and reliability of

Precision Assessment of Artec Space Spider 3D Handheld Scanner for Quantifying Tufa Formation Dynamics on Small Limestone Plates

(PLs)

facial ex-pressions using 3D handheld scanner. Journal

of Cranio-Maxillofacial Surgery, 47(6), 895-901.

Patel, A., Islam, S. M. S., Murray, K., Goonewardene, M.

S. (2015). Facial asymmetry assessment in adults using

three-dimensional surface imaging. Progress in

orthodontics, 16(36), 1-9.

Reichert, J., Schellenberg, J., Schubert, P., Wilke, T. (2016).

3D scanning as a highly precise, reproducible, and

minimally invasive method for surface area and volume

measurements of scleractinian corals. Limnology and

Oceanography: Methods, 14(8), 518-526.

Ritschl, L. M., Wolff, K. D., Erben, P., Grill, F. D. (2019).

Simultaneous, radiation-free registration of the

dentoalveolar position and the face by combining 3D

photography with a portable scanner and impression-

taking. Head & face medicine, 15(1), 1-9.

Santos, M. C., Souza, C. B., de Freitas, S. R. (2000). A

practical evaluation of the GPS rapid static method.

Geomatica, 54(4), 425-432.

Seminati, E., Canepa Talamas, D., Young, M., Twiste, M.,

Dhokia, V., Bilzon, J. L. (2017). Validity and reliability

of a novel 3D scanner for assessment of the shape and

volume of amputees’ residual limb models. PLoS One,

12(9), e0184498.

Sivanandan, J., Liscio, E., Eng, P. (2017). Assessing

structured light 3D scanning using Artec Eva for injury

documentation during autopsy. J Assoc Crime Scene

Reconstr, 21, 5-14.

URL 1 http://docs.artec-group.com/as/15/en/process.html

#sec-global-optimization, 25 January, 2021.

URL 2 https://www.artec3d.com/cases/measuring-shifts-

in-facial-soft-tissues, 8 February, 2021

Varga, M., Morrison, S. C., Price, C. (2019, October).

Reliability of Measuring Morphology of the Paediatric

Foot Using the Artec Eva Hand Held Scanner. In

Proceedings of 10th Int Conf and Exh on 3D Body

Scanning and Processing Technologies, Hometrica

Consulting, 21-22 October 2019, Lugano, Switzerland,

pp. 22-23.

Vázquez-Urbez, M., Arenas, C., Sancho, C., Osácar, C.,

Auqué, L., Pardo, G. (2010). Factors controlling

present-day tufa dynamics in the Monasterio de Piedra

Natural Park (Iberian Range, Spain): depositional

environmental settings, sedimentation rates and

hydrochemistry. International Journal of Earth

Sciences, 99(5), 1027-1049.

Verhulst, A., Hol, M., Vreeken, R., Becking, A., Ulrich, D.,

Maal, T. (2018). Three-dimensional imaging of the

face: a com-parison between three different imaging

modalities. Aesthetic surgery journal, 38(6), 579-585.

Wang, P., Jin, X., Li, S., Wang, C., Zhao, H. (2019, April).

Digital Modeling of Slope Micro-geomorphology

Based on Artec Eva 3D Scanning Technology. In IOP

Conference Series: Earth and Environmental Science,

Volume 252 - 4th International Conference on

Environmental Science and Material Application 15–16

December 2018, Xi'an, China, No. 5, p. 052116, IOP

Publishing.

Winkler, J., Gkantidis, N. (2020). Trueness and precision of

intraoral scanners in the maxillary dental arch: An in

vivo analysis. Scientific reports, 10(1), 1-11.

Zhang, W., Kosiorek, D. A., Brodeur, A. N. (2020).

Application of Structured ‐ Light 3 ‐ D Scanning

to the Documentation of Plastic Fingerprint

Impressions: A Quality Comparison with Traditional

Photography. Journal of Forensic Sciences,

65(3), 784-790.

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