3D Reconstruction and Visualization of Alternatives for

Restoration of Historic Buildings

A New Approach

Lemonia Ragia

Froso Sarri

and Katerina Mania

School of Architectural Engineering, Technical University of Crete, Kounoupidiana, Chania, Greece

Department of Electronic and Computer Engineering, Technical University of Crete, Kounoupidiana, Chania, Greece

esarri@isc.tuc.gr, k.mania@ced.tuc.gr

Keywords: 3D Reconstruction, Geodetic Measurements, Computer Graphics.

Abstract: This paper puts forward a 3D reconstruction methodology applied to the restoration of historic buildings

taking advantage of the combined speed, range and accuracy of a total geodetic station. The measurements

of geo-referenced points produced a fully interactive and photorealistic geometric mesh of an historic

monument named ‘Neoria’. ‘Neoria’ is a Venetian building located by the old harbour at Chania, Crete,

Greece. The integration of tacheometry acquisition and computer graphics puts forward a novel integrated

software framework for the accurate 3D reconstruction of a historical building. The main technical

challenge of this work was the production of an accurate 3D mesh based on a sufficient number of

tacheometry measurements acquired fast and at low-cost. Interpolation methods ensured that a detailed

geometric mesh was constructed based on a few points. Advanced interactive functionalities are offered to

the user in relation to identifying restoration areas and visualizing the outcome of such works in a fully

interactive application based on game engine technologies. Moreover, the user could photorealistically

visualize the actual or restored monument and calculate distances between points.

1 INTRODUCTION

During the last decade, the heritage community has

witnessed an increase in three-dimensional (3D)

photorealistic reconstructions of archaeological

structures which focused more on pleasing visuals

often overriding the question of scientific accuracy.

Assisted by advances in technology and cheaper

technical costs, museums, archival institutions and

heritage attractions have been frequently developing

digital content. However, archaeologists and

computer scientists have urged caution in the

abundant use of inaccurate and rushed 3D

reconstructions because of the possibility of

misleading the public. 3D reconstruction methods

should promote and ensure that scientific and

historical accuracy is driving such developments.

The ability to reconstruct a historical building’s

current or past structure is an invaluable tool which

could drive its potential restoration.

Restoration of historic buildings is conducted by

a team of historians, archaeologists and engineers

working together to provide restoration solutions

after the diagnosis and evaluation of each restoration

issue. 3D digitization of current historical structures

and 3D reconstruction of restoration areas should be

based on accurate on-site measurements (Sifniotis et

al., 2010). Tacheometry acquisition methods

(Boochs et al., 2006) use modern total stations that

result in high accuracy point measurements as well

as quick and efficient spatial data acquisition in

order to create a geometric mesh of existing

geometry structures. The aim of realistic image

synthesis and computer graphics techniques is the

creation of accurate, high quality imagery that

faithfully represent the geometric structure and light

propagation in a physical environment, the ultimate

goal being to create a 3D visualization which is

perceptually indistinguishable from an actual scene.

Reliable geometric measurements are necessary to

create a canvas of geo-referenced co-ordinates

which could be manipulated by expert users. In this

paper, the integration of tacheometry acquisition and

computer graphics puts forward a novel integrated

software framework for the scientifically accurate

3D reconstruction of an historical building in Crete,

Greece. Initially, a detailed 3D geometric model of

Ragia L., Sarri F. and Mania K..

3D Reconstruction and Visualization of Alternatives for Restoration of Historic Buildings - A New Approach.

DOI: 10.5220/0005376700940102

In Proceedings of the 1st International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM-2015), pages

94-102

ISBN: 978-989-758-099-4

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

the monument is created based on acquired

terrestrial points on-site. Novel interpolation

methods ensure that a detailed geometric mesh is

constructed fast based on far fewer points available

compared to laser scanning. The geometric mesh is

lit utilizing modern global illumination algorithms.

Advanced interactive functionalities are offered to

the user based on game engine technologies. The

user can interactively manipulate as well as navigate

the geometric model. Based on adequate point

acquisition, the user can determine the position and

extent of erosions in the material surfaces, whether

the surface is smooth or includes bumps, whether

there are cleaved and damaged pieces or cracks and

the size of determined areas that subsequently

require full restoration. It can also be investigated

whether the edges of the walls and roof are vertically

aligned or whether there are related discrepancies.

The goal is to offer to the expert user an interactive

scientific tool which determines whether the

building needs maintenance and what interventions

need to be pursued.

1.1 Motivation

The building named ‘Neoria’ (Figure 1) is a

beautiful historic building located at the old harbor

of the old city of Chania, Greece. It was completed

in 1599 by the Venetians, consisting of seven

continuous domes and used for ship repair during

the winter time.

Figure 1: Neoria Historical Building, Chania, Crete.

The current state of this monument is

compromised as it stands a few meters from the

water facing, at times, adverse climate conditions.

As shown in Figure 2, parts of the monument’s stone

walls have fallen, entire sections of walls are loose

and certain edges of the building are partially or

entirely destroyed. Significant structural elements

are impaired and, as a result, Neoria is slowly losing

its original beauty. Cracks are now appearing and

restoration efforts had been limited or wrongly

conducted. For instance, in order to repair cracks,

craftsmen had often applied unsuitable modern

materials such as cement. The outcome of such

badly managed restorations was that at times, repairs

had destroyed neighboring elements to the

restoration areas rather than preserving them.

Figure 2: Damaged areas of Neoria in need of restoration.

The scope of this work is to provide

archaeologists with a technological and relatively

low-cost solution which will enable them to

reconstruct the monument at its current state fast.

Tacheometry measurements on-site are part of a

detailed dataset of the monuments facades and

interior. Such measurements could provide a

geometric mesh which enables expert users to

further inspect the digitally reconstructed monument

in detail and at every angle when planning its

restoration. Unlike time-consuming and lengthy

laser scanning processes, the methodology proposed

is fast, low-cost and of high scientific accuracy. In

this paper, we present the integrated software

framework based on game engine technologies for

the scientific 3D reconstruction and visualization of

Neoria in Chania, Crete employing a modern total

station and advanced computer graphic techniques.

1.2 Previous Work

Two 3D data acquisition methods have been widely

documented in research literature as part of the 3D

reconstruction process of archeological monuments:

photogrammetry and laser scanning.

Photogrammetry is a widely-adopted image-based

process requiring specialized hardware and software

such as a non-standard digital camera for

photogrammetry and a digital station (Gruen, 1998);

(Mayer et al., 2004); (Kersten, 2006); (El Hakim et

al., 2007); (Schaich, 2004). It is widely used for the

3D digitization of historical monuments (Lingua et

al., 2003). Although photogrammetry’s end result is

a high-resolution 3D model including geometric data

for restoration, it is a complex procedure that

requires specialized equipment and software. Dark

shadows and low luminance areas of structures

prevent efficient data acquisition resulting in

uncompleted geometry. On the other hand, laser

3DReconstructionandVisualizationofAlternativesforRestorationofHistoricBuildings-ANewApproach

scanning technology extracts high resolution point

clouds and it is widely used for the scientific

documentation of historical monuments (Boehler et

al., 2004). 3D laser scanning allows the

reconstruction of individual parts of historical

monuments producing a fine geometry mesh (Allen

et al., 2003). Laser scanning supports the

maintenance, preservation and restoration of

monuments based on a highly detailed spatial data

set (Balzani et al., 2004); (Julia et al., 2010). Both

photogrammetry and laser scanning create an

accurate geometric mesh and provide rich detail.

However, in the process, both produce a large

amount of data and the post processing is quite

complicated (Grussenmeyer et al., 2008). In our

approach we use as an alternative geodetic total

stations which are portable, inexpensive, widely

available, requiring less training and having a wide

range of other applications. The goal of this

approach is to deploy a relatively low-cost total

station in order to acquire the co-ordinates of

specific surface points of Neoria in Chania’s harbour

area. The advantage of using a total station to

acquire measurements on-site is that it performs fast

digital data collection of spatial co-ordinates of

archaeological monuments. It is an optical

instrument used to measure horizontal and vertical

angles in order to determine relative position. The

main technical challenge was to produce an accurate

3D geometric mesh based on an efficient amount of

spatial co-ordinates that the total station captured in

the field. The end-goal was to implement an

interactive visualization framework based on game

engine technologies enabling experts to identify

areas for restoration.

1.3 Methodology

In this paper, we propose an optimized tacheometry

surveying technique employing a total station for the

monument surveying. The measurement technique

put forward is quick, flexible and accurate, requiring

a small number of points acquired in interest areas

which at the final stage of processing are connected

to form a geometric mesh (Andrews et al., 2009). If

needed, additional points can be easily acquired in

places and used in conjunction with previously

acquired datasets.

The 3D model is constructed by connecting the

acquired 3D points in order to reconstruct a

geometric mesh of a surface. Surface reconstruction

algorithms take as input a set of sample points that

describe the shape or topology of an object in three

dimensions and convert these points into a 3D

model. There is a vast variety of algorithms which

reconstruct the topology surface from sample points

in three dimensional space. The input of such

algorithms is a set of 3D coordinates utilized to

construct a polygonal mesh. A mesh consists of

vertices, edges and polygons. The 3D points are

considered vertices forming edges and polygons.

Reconstruction algorithms which compute

geometries either create the mesh using the existing

points as vertices or employ implicit functions to

approximate the surface. Algorithms utilizing

implicit surfaces documented in research literature

are the Poisson surface reconstruction (Kazhdan et

al., 2006) and Hoppe’s algorithm (Hoppe et al.,

1992). Popular algorithms which compute

geometries are the powercrust algorithm (Amenta et

al., 2001) and the ball pivoting algorithm

(Bernardini et al., 1999). After the surface is

reconstructed, subdivision is often needed.

Subdivision surfaces are polygon mesh surfaces

generated from a starting mesh through an iterative

process that levels the mesh while increasing its

density. Complex smooth surfaces can be derived in

a reasonably predictable way from relatively simple

meshes. There are two kinds of subdivision

schemes; approximating subdivision surfaces and

interpolating subdivision surfaces. Approximating

means that the result surfaces approximate the initial

meshes and that after subdivision, the newly

generated points are not included in the initial

surfaces. Examples of such subdivision algorithms

are Catmull–Clark (Catmull and Clark, 1978), and

Doo-Sabin subdivision surface (Doo and Sabin,

1978). Interpolating subdivision means that after the

subdivision, the control points of the original mesh

are interpolated to form the resulting surface. A

popular method of interpolating subdivision is the

butterfly subdivision (Zorin et al., 1996). Although

widely used when dealing with laser scanning point

clouds, these algorithms require dense point clouds

to produce an accurate model. The workflow of 3D

reconstruction based on a rather small amount of

acquired measurements utilizing a total station is

detailed in the following sections.

2 RECONSTRUCTION IN 3D

2.1 Data Acquisition

The outlines of the seven buildings of the Neoria

complex were initially measured, along with the

edges of the doors and the windows. Due to the

extent of the erosion, the framework proposed

GISTAM2015-1stInternationalConferenceonGeographicalInformationSystemsTheory,ApplicationsandManagement

provides measurements of the angles and distance to

surveyed points recorded digitally. Subsequently, the

x, y and z coordinates of each point were calculated

and connected, offering a 3D model of the

monument. A distance accuracy of ±2mm to ±5mm

was inherent to each point. In order to survey the

whole monument, the total station acquired

measurements based on selected positions called

‘stations’. The number of spatial points that were

measured was about 780.

2.2 Data Processing

The raw data were acquired in the form of the Greek

geodetic system coordinates. Civil3d by Autodesk,

processes geodetic data in the form of coordinates as

input and converts them to 3D points. The raw data

were imported as Greek Geodetic Reference System

GGRS 87 coordinates. A survey database was

created and the points were treated as point groups,

processed concurrently or in parts as needed. The

coordinates acquired are now treated as 3D points in

space, forming a sparse 3D point cloud. The main

technical challenge of this phase was to produce an

accurate 3D mesh based on a low resolution 3D

point cloud. The spatial data acquisition method

adopted, although quick and efficient, it doesn’t

provide a large volume of information in the form of

a high resolution point cloud that other methods

such as laser scanning achieve. We will explain, in

the following sections, how an accurate geometric

mesh was produced based on a sufficient number of

geodetic measurements rather than on a dense, often

complex, point cloud.

2.3 Surface Reconstruction

Surface reconstruction algorithms vary depending on

how they handle available spatial data (Tang et al.,

2013). In order to reconstruct a 3D surface based on

total station measurements, approximating

algorithms were quickly dismissed. These

algorithms require dense point clouds to produce an

accurate model. In this case here, the exact geodetic

measurements acquired in the field were to be

interpolated in order to produce a finer geometric

mesh. An appropriate interpolation algorithm would

take as input a sufficient amount of points recorded

via the geodetic total station and identify spatial

areas where interpolation is needed in order to fill in

gaps appearing in-between measurements. An

example of the implementation of the ball pivoting

algorithm as applied to a small subset of the total

station measurements is shown in Figure 3. There

are holes and geometry discontinuities due to lack of

surface information and varying density of the data.

The best approach would be to use Delaunay

triangulation to form a Triangular Irregular Network

(TIN surface) (Lee and Arthrur, 1986);

(Edelsbrunner, 2000).

Figure 3: Implementation of the ball-pivoting algorithm.

Holes and discontinuities are visible.

2.4 Delaunay Triangulation

Given a set of data points, Delaunay triangulation

produces a set of lines connecting each point to its

natural neighbors. The Triangular Irregular

Networks are used to represent surfaces in

Geographic Information Systems (GIS - earth

surfaces). In order to produce surfaces consisting of

the four walls of the building, the axis of data was

rotated 90 degrees facing upwards. The points

belonging to each wall were processed separately.

The x, y, z dimensions of each wall were designated

appropriately to achieve the desired rotation. After

implementing the necessary changes, four separate

groups of data were derived subsequently processed

as TIN surfaces. The points were connected to form

triangles according to the Delaunay methodology.

These networks were used as base surfaces (Figure

4), however, further processing was necessary in

order to add further surface detail so that the real-

world surface is approximated.

Figure 4: Delaunay Triangulation of the survey points.

2.5 Surface Processing

Initially, each wall was rotated so that they were

correctly aligned attached to each other, forming the

complete mesh of the monument. Depth information

was integrated around the monument’s windows and

doors and the mesh was ‘tweaked’ wherever needed

in order to correct limited mesh discontinuities. Each

3DReconstructionandVisualizationofAlternativesforRestorationofHistoricBuildings-ANewApproach

side of the building was processed and then

subsequently aligned to form the complete building

(Figure 5, 6).

At this point, a basic shape of the outline of the

monument including details of its structure was

completed. In order to integrate further detail, the

surface was further subdivided. It was significant to

maintain the original position of the vertices so that

an interpolating subdivision could be deployed to fill

in the gaps.

Figure 5: The base surface after corrections (South side).

The depth of the doors and windows is visible.

Figure 6: The North side of Neoria after geometry

processing.

The next step was to take into consideration the

shape and quality of the surface. Large segments of

the monument included insufficient spatial

information because of the small amount of points

recorded. For example, there were sparse blank

spaces between windows. Additional spatial

information around these areas was paramount in an

effort to maintain the monument’s initial shape. It

was evident that interpolating subdivision methods

deform such areas. By not succeeding to find surface

information, they randomly add points in blank

spaces, deforming the surface and negatively

affecting the geometric fidelity of the model. A

simple midpoint subdivision was applied to the

spatial dataset (Chen and Prautzsch 2012). The

subdivision scheme was applied at the point where

every edge was split on its midpoint. A finer mesh

with no distortion was acquired at the end of this

process (Figure 7, 8).

To further approximate the real-world surface,

the subdivided mesh was refined. A finer and

smoother surface enables the application of detailed

texturing on the surface making it more realistic.

The problematic areas such as the circular windows

and doors were initially brought into focus. To

accomplice a realistic circular window, the edges of

the windows were further subdivided by applying a

Meshsmooth modifier in a standard modeling

software, which subdivides the geometry while

interpolating the angles of new faces at corners and

edges. This process resulted in the desired geometric

shape around the problematic areas (Figure 8).

Figure 7: The 3D model after implementing midpoint

subdivision.

Figure 8: Window details after further refinement.

2.6 Texturing

The 3D surface was textured applying images

acquired on-site. In order to begin the process of

texturing, the current, largely un-restored condition

of the monument was taken into account. There are

cracks, damages and alterations on the walls. Parts

of the brick walls are missing and there are add-ons

of modern materials applied to certain parts. The

challenge was to apply such complex effects on the

available plane polygonal faces. For this purpose,

multi-texturing was employed. The primary set of

textures included mostly images taken on the field.

Normal mapping was applied which simulated the

bumps and abnormalities of the surface by detecting

the edges of the surface’s constructed elements such

as bricks, break lines, etc. offering the illusion of an

uneven surface.

UV mapping is the process of forming a 2D

image representation out of a 3D model, in order to

correctly project a texture map on the model. During

this process, the model’s derived 3D mesh was

unwra90pped to a flat 2D image. This process

involved splitting the geometric mesh into clusters

by selecting similarly-looking parts and faces. In

each cluster, the appropriate texture was applied as a

diffuse map. The diffuse map is a texture used to

define the surface's main colour. It sets the tint and

GISTAM2015-1stInternationalConferenceonGeographicalInformationSystemsTheory,ApplicationsandManagement

intensity of diffuse light reflection on the surface. A

simple diffuse map, however, does not communicate

photorealism. The perception of realism as well as

3D depth is attained by normal mapping. Normal

mapping simulates small displacements of the

surface while the surface geometry is not modified.

Instead, only the surface normal is modified as if the

surface had been displaced. This is achieved by

perturbing the surface normal of each object and

using the perturbed normal during lighting

calculations.

Normal maps consist of red, green, and blue.

These RGB values translate to x, y, and z

coordinates, allowing a 2D image to represent depth.

The 2D image is applied to the surface. This way, a

3D surface simulates the lighting and ultimately the

color associated with 3D coordinates. In the normal

map, each pixel's color value represents the angle of

the surface normal.

After texturing, the model was imported in the

Unity3D game engine. Game engines have become a

tremendous asset in archaeological and architectural

visualization. Unity3D is a powerful cross-platform

game engine developed by Unity Technologies. It is

used not only to develop video games, but also for

any kind of sophisticated 3D visualization which can

be ported to the web or on any mobile platform.

2.7 Lighting

Photorealism relies heavily on lighting. After

texturing, it is essential to apply a global

illumination algorithm so that the lighting of the

scene simulates real-world lighting conditions.

Unity3D uses the Beast lighting algorithm. Unity

Pro extends this functionality by Global

Illumination, allowing for the so-called ‘baking’ of

realistic and beautiful lighting, that would otherwise

be impossible in real-time.

Global illumination represents a group of

algorithms used in 3D computer graphics offering

physics-based rendering by emulating inter-

reflections in a scene. Such algorithms take into

account not only the light which comes directly from

a light source (direct illumination, local

illumination), but also perceives all surfaces in the

scene as lights sources which, in turn, affect the

lighting of other surfaces, whether reflective or not

(indirect illumination). In fact, this is a recursive

process which should be terminated when the global

illumination algorithms are utilized. It is user-

defined when the algorithm will terminate; in most

cases this occurs when subsequent iterations do not

produce perceivable additional lighting effects and

shadowing compared to the previous ones. Diffuse

inter-reflection is a global illumination effect applied

to architectural visualization and to the interior or

exterior of buildings producing beautiful subtle

shadowing and color-bleeding effects, integrated in

the radiosity global illumination algorithm. The

Beast lighting algorithm is a radiosity-like renderer

that enhances the photorealism of the geometric

mesh and offers a realistic as well as scientifically

accurate simulation of the Neoria monument

(Figures 9, 10).

Figure 9: The North side of the Neoria monument

photorealistically rendered.

Figure 10: The South side of the Neoria monument

photorealistically rendered.

3 DIGITAL RESTORATION

The goal of this work was to develop an interactive

visualization tool which offers expert users the

capability of interacting with a scientific 3D

reconstruction of an existing monument acquiring

information concerning its future restoration. The

visual impact of such restoration is rendered after

user interaction and presented to the user. Unity3D

offers sophisticated tools for the development of 3D

interactive applications and a programming interface

utilized for the development of complex geometry

behaviors, user interaction, etc. The final

application’s functionalities were programmed in

Javascript.

3DReconstructionandVisualizationofAlternativesforRestorationofHistoricBuildings-ANewApproach

3.1 3D Interactive Visualization

Interactive functionalities are provided to the user

who dynamically manipulates as well as navigates

the geometric model (Figure 11). The expert user

can zoom on parts of the geometry for closer

inspection and identify interest areas relevant to

monument maintenance, erosion, wall alignment,

cracks, measurements, etc.

Figure 11: Interactive walkthrough of the monument and

sea.

Figure 12: Currently, there is a missing part on top of the

building.

Advanced functionalities allow the user to alter

the geometry provided. This operation is

implemented in damaged areas which are in need of

restoration represented as geometric mesh (Figure

12, 13). For instance, added cement can be removed

and replaced visualized by acquiring textures

derived from non-damaged areas. Hotspots of

interest and restoration areas can be activated

through the on-screen menu. By interacting with the

menu, the user can toggle between surfaces; for

example, the user can visualize the monument

surface as it exists today and the restored surface

rendered in red color (Figures 13, 14). In order to

render the restored surface, the appearance of the

non-damaged parts was taken into consideration, as

well as the measurements of structural elements of

neighbouring areas of interest. This process

recreated missing surfaces while there was lack of

architectural information.

Figure 13: Digital restoration of the missing part; the red

part does not exist at present.

Figure 14: Currently, the door opening is blocked.

Figure 15: Door opening restored as appearing in the past.

Scientific information is offered to the expert

user in relation not only to potentially restored areas

but also to measurements between areas, existing

and restored. The user could select specific points on

the surface and the system calculates the distance

between them. Moreover, the user could actually

GISTAM2015-1stInternationalConferenceonGeographicalInformationSystemsTheory,ApplicationsandManagement

100

visualize the co-ordinates of the original tachometry

measurements and identify areas where the points

are dense or scarce (Figure 16). The user could also

visualize the current condition of the monument lit

under different lighting conditions. In order to aid

further the specialists in restoration to extract

architectural information, a functionality which can

display information was implemented. This

operation can be chosen from the screen menu,

prompting the user to pick two points anywhere on

the model and information regarding the position

and relation of the chosen points is displayed. Their

distance in 3d space is calculated, as long as their

depth difference on the vertical surface (Figure 17).

Figure 16: Display of original tacheometry measurements

(green points) and their coordinates.

Figure 17: Distance calculated between points. The upper

corners of a window were chosen for measurement.

The units are in meters. In that way each user can

measure the dimensions of a particular feature or can

gain information on the size and exact place of a

restored part.

4 CONCLUSIONS

In this paper, the integration of tacheometry

acquisition and computer graphics puts forward a

novel integrated software framework for the

scientifically accurate 3D reconstruction of a

historical building in Crete, Greece. The interactive

framework presented was based on game engine

technologies which provide powerful tools for

photorealistic visualization. The main technical

challenge of this work was the production of a

scientifically accurate 3D mesh based on a rather

small number of tacheometry measurements

acquired fast and at low-cost. Novel interpolation

methods ensured that a detailed geometric mesh was

constructed fast based on far fewer points available

compared to laser scanning. Advanced interactive

functionalities are offered to the user in relation to

identifying restoration areas and visualizing the

outcome of such works. Moreover, the user could

visualize the co-ordinates of the points measured,

calculate distances at will and navigate the complete

3D mesh of the monument. Future work will

produce an advanced visualization system operated

on a mobile platform, investigating the effectiveness

of complex visualization techniques linked to

diverse restoration strategies by local authorities,

visualizing the visual impact of such restorations.

ACKNOWLEDGEMENTS

We would like to thank Anastasia Tzigounaki,

serving as the director of the 15th Ephorate of

Prehistoric and Classical Antiquities in Chania,

Greece, for her contribution to this work.

REFERENCES

Andrews, D, Bedford, J, Blake, B and Bryan, P., 2009.

Metric Survey Specifications for Cultural Heritage, 2

edition. Swindon: English Heritage.

Allen, P., K., Troccoli, A., Smith, B., Murray, S., Stamos,

I., and Leordeanu, M., 2003. New methods for digital

modeling of historic sites. IEEE Computer Graphics

and Applications 23, no. 6: 32-41.

Amenta, N., Choi, S., & Kolluri, R. K., 2001. The power

crust. In Proceedings of the sixth ACM symposium on

Solid modeling and applications, pp. 249-266.

Balzani, M., Santopuoli, N., Grieco, A., & Zaltron, N.

2004. Laser scanner 3D survey in archaeological field:

The Forum of Pompeii. In International Conference

on Remote Sensing Archaeology, Beijing, pp. 169-175.

Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., &

Taubin, G., 1999. The ball-pivoting algorithm for

surface reconstruction. Visualization and Computer

Graphics, IEEE Transactions on, 5(4), 349-359.

3DReconstructionandVisualizationofAlternativesforRestorationofHistoricBuildings-ANewApproach

101

Boehler, W., Vicent, M. B., Heinz, G., Marbs, A., &

Müller, H., 2004. High quality scanning and modeling

of monuments and artifacts. InProceedings of the FIG

Working Week, pp. 22-27.

Boochs, F., Hoffmann, A., Huxhagen, U., and D. Welter,

D., 2006. Digital reconstruction of archaeological

objects using hybrid sensing techniques-the example

Porta Nigra at Trier. BAR INTERNATIONAL SERIES

1568: 395.

Catmull E. and Clark J., 1978. Recursively Generated B-

Spline Surfaces on Arbitrary Topological Meshes,

Computer Aided Design, vol. 10, no. 6, pp. 350-355.

Chen, Q., and Prautzsch, H., 2012. General Midpoint

Subdivision. arXiv preprint arXiv:1208.3794.

Doo, D., and Sabin, M., 1978. Behaviour of recursive

division surfaces near extraordinary points. Computer-

Aided Design, 10(6), 356-360.

Edelsbrunner, H., 2000. Triangulations and meshes in

computational geometry. Acta Numerica 9, 133-213.

El-Hakim, Gonzo L., Voltolini F., Girardi S., Rizzi R.,

Remondino F., Whiting E., 2007. Detailed 3D

Modelling of Castles. International Journal of

Architectural Computing, Vol. 5(2), pp. 199-220.

Gruen, A., 1989. Digital photogrammetric processing

systems- Current status and prospects.

In (International Society for Photogrammetry and

Remote Sensing, Congress, 16 th, Kyoto, Japan, July

1-10, 1988) Photogrammetric Engineering and

Remote Sensing, Vol. 55, pp. 581-586.

Grussenmeyer, P., Landes, T., Voegtle, T.,and Ringle, K.,

2008. Comparison methods of terrestrial laser

scanning, photogrammetry and tacheometry data for

recording of cultural heritage buildings. In ISPRS

Congress Proceedings, Beijing, pp. 213-18.

Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., &

Stuetzle, W., 1992. Surface reconstruction from

unorganized points Vol. 26, No. 2, pp. 71-78.

Julia, A., G., Belén R., R., Diego G., A., and M. Teresa R.,

B., 2010. Terrestrial laser scanning intensity data

applied to damage detection for historical

buildings. Journal of Archaeological Science37, no.

12: 3037-3047.

Kazhdan, M., Bolitho, M., & Hoppe, H., 2006. Poisson

surface reconstruction. In Proceedings of the fourth

Eurographics symposium on Geometry processing.

Kersten, T., 2006. Combination and Comparison of

Digital Photogrammetry and Terrestrial Laser

Scanning for the Generation of Virtual Models in

Cultural Heritage Applications. The 7th International

Symposium on Virtual Reality, Archaeology and

Cultural Heritage, VAST. pp. 207–214, M. Ioannides,

D. Arnold, F. Niccolucci, K. Mania (Editors).

Lee, D., T., and Arthur K., L., 1986. Generalized

Delaunay triangulation for planar graphs. Discrete &

Computational Geometry 1.1 201-217.

Lingua, A., M., Piumatti, P., and Rinaudo, F., 2003.

Digital photogrammetry: a standard approach to

cultural heritage survey. International Archives of the

Photogrammetry, Remote Sensing and Spatial

Information Sciences 34.Part 5: 210-215.

Mayer H., Mosch M., Peipe J., 2004. 3D model generation

and visualization of Wartburg Castle ISPRS Int.

Workshop on Processing and Visualizaation Using

High-Resolution Imagery. Vol (36) PART 5/W1, 18-

20 November, Pitsanulok Thailand.

Schaich, M., 2004. 3D Scanning Technologies and Data

Evaluation in an Archaeological Information

System. Proceedings of the CAA. 2004.

Sifniotis, M., Jackson, B., Mania, K., Watten, P.L., White,

M., 2010. 3D Visualization of Archaeological

Uncertainty. Poster, ACM Siggraph Symposium on

Applied Perception in Graphics and Visualization

2010, Los Angeles, USA.

Tang R., Halim S., Zulepli M., H., 2013. Surface

Reconstruction Algorithms Review and Comparison.

Zorin, D., Schroder, P., and Sweldens, W., 1996.

Interpolating subdivision for meshes with arbitrary

topology. In Proceedings of the 23rd annual

conference on Computer graphics and interactive

techniques (pp. 189-192) ACM.

GISTAM2015-1stInternationalConferenceonGeographicalInformationSystemsTheory,ApplicationsandManagement

102