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Benchmarking Range-Based and Image-Based Techniques for Digitizing a Glazed Earthenware Frieze

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3D high resolution models can be produced both using range-based and image-based techniques. In this work, we evaluate the performance of the Photoscan commercial software in a challenging project: the digitization of an earthenware frieze of about 36 m in length. In order to choose the most effective technique and to define the best workflow for on-the-field data acquisition and the subsequent data elaboration, some tests were performed on a portion of the frieze. We discuss the results and compare the models resulting from different workflows with a reference data set taken from the scan data.
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BENCHMARKING RANGE-BASED AND IMAGE-BASED TECHNIQUES FOR
DIGITIZING A GLAZED EARTHENWARE FRIEZE
G. Tucci* , V. Bonora, A. Conti, L. Fiorini
GECO Lab., University of Florence - DICEA Dept., Via P.A. Micheli, 8, Florence
grazia.tucci@unifi.it
KEY WORDS: Laser scanning, Photogrammetry, Comparison, Point Cloud, 3D models
ABSTRACT:
3D high resolution models can be produced both using range-based and image-based techniques. In this work, we evaluate the
performance of the Photoscan commercial software in a challenging project: the digitization of an earthenware frieze of about 36 m
in length. In order to choose the most effective technique and to define the best workflow for on-the-field data acquisition and the
subsequent data elaboration, some tests were performed on a portion of the frieze. We discuss the results and compare the models
resulting from different workflows with a reference data set taken from the scan data.
1. RESEARCH AIM
In this work we intend to evaluate the performances of range-
and image-based systems in order to produce highly detailed 3D
models for use by restorers and other heritage experts to map
their diagnostic analysis and record their interventions on them
(Tucci et al. 2015) and make a 3D print for a permanent
exhibition. The artefact for digitization is a frieze; its
dimensions suggest that it be considered an architectural survey,
but closer evaluation highlights that this project requires a
greater level of detail.
Before starting the (still ongoing) digitization process, we made
some tests on a portion of the frieze in order to choose the most
effective technique, and to plan the best further workflow for
data acquisition and elaboration. We present some data
evaluation results, relating both to laser scanner and
photogrammetric surveys. Considering the aim of the project,
our main focus was not on accuracy (even though we present
some considerations in 6.1) but we preferred to analyse more
qualitative aspects.
2. INTRODUCTION
“Sampling-based” forms of description have been widely used
in the past for DTM production with photogrammetric
techniques. Since moving from analytical to digital
photogrammetry, the assisted stereo-plotting of points regularly
spaced in x and y has become an automatic procedure thanks to
the development of stereo-matching algorithms (Ackermann
1994). At first, algorithms applying feature-based strategies
were developed, in order to provide correspondences with a
high level of certainty and to limit computational resources
(Foerstner 1986). The matching algorithms now implemented in
software tools for image-based 3D model generation are based
on stereo-matching or multi-view approaches and allow a dense
point cloud to be obtained from a dataset of unordered images
(Remondino et al. 2014).
At the same time, laser scanning is a consolidated technique for
sourcing dense point clouds. Obviously both techniques have
pros and cons and it is important to compare not only the results
(in terms of accuracy or more subjective points of view) but to
also consider the recording/processing time ratio, the cost of the
equipment, and the possibility of using the same instrument to
document objects of different sizes and with different levels of
detail (Guarnieri et al. 2010).
Nowadays, it is common to refer to models with a considerably
different resolution (i.e. the mean distance between closest
points) as “dense point clouds”: if we are to consider a terrain
model from elaboration of aerial images, for example, a dense
point cloud could have a 0.5 m resolution, while for a close-
range project in the architectural or archaeological field it is not
unusual to be dealing with centimetric to sub-millimetric
resolutions.
3. A GLAZED EARTHENWARE FRIEZE: A
CHALLENGING ARTEFACT TO DIGITIZE
The Ospedale del Ceppo in Pistoia, founded in 1277, was
embellished around 1512 by a porch reminiscent of the Loggia
degli Innocenti in Florence. The three sides of this loggia were
decorated with a series of glazed earthenware artworks which
make this monument one of the largest and most significant
examples of this technique. The components of the frieze are
attributed to various artists (Marquand 1918, Tigri 1833).
Around 1510-15, Benedetto Buglioni was commissioned to
make a coat of arms. Most of the great frieze (43 m x 1,5 m) on
the south and west façades is attributed to Santi Buglioni
(Benedetto’s nephew). In an original mix of realistic details and
subtle theological references, the charitable works carried out
by the hospital are depicted as a genuine representation of the
Seven Works of Mercy. The scenes are separated by allegories
of the virtues (Prudence, Faith, Charity, Hope and Justice), and
a Siren supports the hospital crest at each corner. Filippo di
Lorenzo Paladini is thought be the author of the last scene,
made around 1585 with a different style and technique (cold-
painted unglazed earthenware). The medallions in the spandrels
of the arches of the loggia depict garlands containing scenes of
the life of the Virgin or coats of arms and are attributed to
Giovanni della Robbia. However, a closer examination shows
that some coats of arms could have been altered later.
The tests presented in the paper were carried out on the
“Clothing the naked and taking care of widows and orphans”
scene by Santi Buglioni. The artwork, located on the W façade,
is mostly in glazed earthenware, with the exception of the bare
skin of the characters, which is made from unglazed terracotta
with traces of cold painting. The glazed surfaces are very
reflective and alternate large light and black (or dark) areas with
few or very small features, such as cracks and defects. So, both
range-based and image-based acquisition of this artwork results
challenging.
4. RESEARCH METHODOLOGY PIPELINE
1. Project planning
2. Laser scanning survey
3. Photogrammetric survey
4. Photogrammetric data evaluation
5. 3D model comparison and assessment
6. Workflow optimization for continuing the digitization
project
5. PROJECT PLANNING
The surface of the high relief appeared a challenge right from
the beginning of the project since it was deemed problematic
both with the laser scanner – due to the glazed surface coat
and with photogrammetric systems based on matching
algorithms – due to the quite uniform texture and the lack of
features (Lichti 2002; Guidi et al, 2009; Godin et al., 2001;
Nicolae et al. 2014).
Therefore, the aim of the first studies on the frieze was to test
different techniques – both range- and image-based – in order to
evaluate their usefulness in the project to digitize the whole
artwork. Logistical aspects conditioned the work on the field
since the frieze is about 10 metres above than the ground level
and scaffolding has been erected for restoration work.
Moreover, some architectural drawings of the façades were
urgently required, therefore we prepared some orthoimages
from scan data at 1:50 scale, integrated with architectural details
in AutoCAD (Figure 1).
5.1 Preliminary steps
5.1.1 Laser scanning: choice of the equipment: A time-of-
flight and a phase-shift laser scanner were available for the test:
a C10 by Leica Geosystems and a 5010C by Zoller + Fröhlich.
Some additional tests were performed later, by scanning on the
scaffolding at a close distance using a Leica Geosystems
HDS6000 phase-shift TLS.
5.1.2 Photogrammetry: camera/lens choice and
calibration: We planned to take the photos with an SLR camera
(D700 by Nikon); considering the space available in front of the
frieze on the scaffolding, and in order to fully exploit the sensor
resolution we planned to use a 50 mm lens. As a preliminary
operation we calibrated the camera+lens, using the multi-sheet
procedure provided by PhotoModeler (EOS Systems). This
software uses a standard lens distortion formulation with four
parameters which is a subsample of the parameter set
introduced by (Brown, 1971); in order to meet the PhotoScan
requirements, we transformed the unbalanced radial lens
distortion to a balanced form (Wiggenhagen, 2002).
5.1.3 Software choice: The laser scanning data were
processed using Cyclone (by Leica Geosystems) and JRC
Reconstructor (by Gexcel). The Photoscan commercial software
(by Agisoft) was used to data process the photogrammetric
project. Mesh models were elaborated using MeshLab (ISTI-
CNR), and model comparisons were made using
CloudCompare.
6. LASER SCANNING SURVEY
6.1 Preliminary considerations on expected accuracy
Assuming that the accuracy of a laser scanner is composed of a
combination of errors in distance and angle measurements, the
effect is distance-related. Since the scan positions were quite
orthogonal to the façade, we can consider that the measured
point accuracy on the frieze mean plane is mainly related to the
angular component. By disregarding accuracy in distance and
assuming a scan distance of about 20 m from the object and
referring to the technical data summarized in Table 2, we
expected to reach an accuracy of:
- Laser scanner C10: ± 6 mm (as declared in the technical
datasheet for a range of 1 to 50 m)
- Laser scanner Z+F 5010C: <± 3 mm
In order to set the higher scan resolution while at the same time
avoiding oversampling we scanned:
- with C10, at a resolution of 7 mm @20 m
- with 5010C, at a resolution of 3 mm @20 m (preselected
“Ultra high” resolution).
Figure 1. The main façade of the Ospedale del Ceppo, with the earthenware frieze
Table 2. Laser scanners technical data
6.2 Work on the field
In a first survey campaign, all the loggia façades were acquired
in four scans made with a ToF Leica Geosystems C10 laser
scanner: Figure 1 shows some graphical output. In the later
campaign, a Z+F 5010C phase-shift scanner was used and eight
scans were acquired, each one aiming to record three arches of
the façade, to acquire a higher-resolution digital model of the
frieze. In both cases, an average distance of about 20 m was
chosen, in order to balance accuracy+level of detail
requirements and favour a quite complete documentation. In
fact, in some parts of the frieze the depicted figures are highly
three-dimensional and they cause some gaps in data in the
scans, which were necessarily acquired from the ground. The
resulting overlapping between adjacent scans ensures a quite
complete documentation.
6.3 Data elaboration
All the scan data were aligned in a local reference system: an
initial solution results from manually selecting pairs of natural
points, then optimization based on ICP algorithm was
performed using Cyclone software. The further elaborations
performed using Cyclone are: a) manual segmentation of the
frieze data with respect to the complete model (some of the
scans were acquired with a 360° field of view, others with a pre-
selected window); b) manual data cleaning in order to remove
the scaffolding and the wooden boards that partially hid the
frieze.
For a visual comparison, meshes were created from the point
clouds acquired from each scanner. As all models proved to be
unsatisfying for the aim of the project (to make a copy of a part
of the frieze with CNC or additive manufacturing techniques),
we preferred to plan a photogrammetric survey. In any case we
decided to use the point model obtained with the 5010C scanner
as a reference system: a set of ground control points (GCPs) and
check points (CPs) were extracted from single aligned scans
(visualizing the intensity value in a scale of greys) and used for
image orientation and subsequent checks (see also Stavropoulou
et al. 2014).
7. PHOTOGRAMMETRIC SURVEY
7.1 Preliminary considerations
General issues arising in our photogrammetric project relate to
poor textures, repetitive patterns, shadows, multi-layered and
transparent objects, radiometric artefacts such as specular
reflections, partial occlusions and brusque discontinuities. The
main part of the frieze is south-facing, therefore in generally
unfavourable lighting conditions; as the test described here
concerns the west façade, photos were shot under natural light,
avoiding direct sunlight. For the time being, we have not dealt
with the matters of colour fidelity or image enhancement
(Ballabeni et al. 2015, Apollonio et al. 2014).
7.2 Work on the field
The average distance between the subject and the camera was
about 1,80 m. The focus of the lens was fixed; the ISO setting
was changed in the 400-1000 range depending on the quite
variable light conditions, the aperture was fixed at f11.
The camera network shown in Figure 3 consists of a series of
normal and convergent images.
Figure 3. Test area camera network
7.3 Data elaboration
The orientation process computes the extrinsic parameters of
the camera. In fact the intrinsic parameters were fixed thanks to
the previous calibration. The features were first detected and
then matched across the image dataset. For close-range projects
no pair-preselection strategy is available (such as for small-scale
projects, where GPS data can be available), which means that a
long computation time is required.
The photos were oriented with the constraints provided by 11
GCPs deriving from the higher resolution scan model in order
to scale and reference the photogrammetric model in the same
reference system; nine more points were used to check the
results.
The tie points generated by a matching process were visualized
as a “sparse cloud” (of about 13.000 points). Starting from the
sparse point cloud, Photoscan’s matching tools generated a
dense point cloud of about 7 million points.
Normal images 60
Oblique images 255
GSD 0,2 mm
Tie points 12 759
TP Projections 72 300
GCPs 11
CPs 9
Table 4. Test area photogrammetric data
8. PHOTOGRAMMETRIC OUTCOME ASSESSMENT
8.1 Image quality
The Photoscan software supplies a parameter for an automatic
assessment of image quality. Considering that hundreds of
photos have to be managed in a project, it would be very helpful
to have a way to easily and quickly find blurred images.
Unfortunately, the “Image Quality” parameter only provides
information on the sharpest border detected on the image;
furthermore, it relates to the entire photo, while it may be that
only a part of it is out of focus - e.g. due to high f-stop setting. It
is therefore only useful for finding images which are obviously
blurred.
8.2 Meshing algorithms
In order to define the best workflow it is sometimes useful to
convert data and process them using different software, since
they may each be more efficient in performing different tasks. In
order to test Photoscan’s capacities, we chose to mesh the same
set of data using Meshlab too. While no information is available
in the Photoscan commercial software about the implemented
algorithms and their settings, the Meshlab open source software
provides good references for them. Meshlab also has different
algorithms for generating meshes from 3D points, among which
the Poisson reconstruction algorithm. It has been progressively
optimized and also a recent improved version has been
developed (Kazhadan 2013). In different software implementing
that algorithm some parameters can be set up: in Meshlab it is
possible to define Octree Depth, Solver Divide and Sample per
Node.
The log provided by Photoscan shows that the meshing process
is based on an octree approach (“depth parameter” is set at 13
by default) and performing a consistent decimation. In fact, a
mesh model of about 7 million triangles (referred as PS in Table
6)was obtained starting from a dense cloud of about 32 million
points.
The dense cloud was then meshed in different ways:
1) a 64-million-face model was achieved through a Poisson
reconstruction algorithm implemented in Meslab (Kazhdan
et al, 2006), later reduced (using the Quadric Edge Collapse
Decimation filter) to the same size as the Photoscan model
(the final model is referred to ML01 in Table 6);
2) by carring out a Poisson Disk Sampling (Corsini et al.,
2012) in advance until a point cloud was obtained that is
half the size of the Photoscan model; in this way the mesh
obtained is about the same size (referred as ML02 in Table
6).
Mesh
Models Artefacts Noisy areas
localization Smooth # points
PS - = - 6 570 k
ML01 + = - 8 005 k
ML02 - = + 5 783 k
Table 6. Assessment of surface models meshed using Photoscan
(PS) and Meshlab (ML01 and ML02)
Some subjective evaluations are summarized in Table 6:
- the PS and ML02 models look alike, therefore it is possible
to suppose that Photoscan uses a similar meshing algorithm
to the one implemented by Meshlab, even though the
resulting model is perhaps optimized by some filters that
aim to remove the large triangles (which are still present in
the Meshlab models, see over the figure’s heads).
- the noise in the point data set seems to affect both tools in
the same way: by looking closely at the figures’ dresses and
the background panels it is possible to recognize a
corresponding spiky effect in all the models.
- the three models are quite similarly smoothed - ML2 is
slightly more smoothed.
9. 3D MODEL COMPARISON AND ASSESSMENT
9.1 Photogrammetry vs laser scanning
3D scanning and photogrammetry can produce qualitatively
comparable results, but combining or comparing the data
quantitatively is not easy. The data resulting from the most
“mature” technology is usually taken as a reference: in the past,
while aiming to test laser scanner instruments, several
researchers evaluated range data using photogrammetric results
(Velios and Harrison 2002, Guidi et al. 2004, Tucci et al. 2004,
Remondino et al. 2005). More recently range data has been
assumed as the reference (estimating its accuracy more on the
basis of empirical experiences than on metric tests): Dellepiane
et al. 2012; Bolognesi et al. 2015, Lerma and Muir 2014,
Koutzoudis et al. 2013.
The aim of our work is to assess the suitability of the
photogrammetric outcome. There is not the ground truth
required to make accuracy tests: if we want to evaluate a
photogrammetric system performance, sufficiently good
reference data is required (three to ten times more accurate). But
considering the high level of detail required by the project and
the dimensions of the object it is very difficult to obtain an
accurate and reliable model while avoiding accuracy loss due to
the alignment process (Beraldin 2004, Gruen 2012).
We carried out two scans of the frieze, with different
instruments; the comparison between the single scans shows
that the datasets are congruent (see Figure 7).
Figure 7. Comparison (based on signed distances) between scan
models obtained with the C10 and 5010C
Therefore we assumed the higher resolution scan as reference
data. All the comparisons made between models were computed
using CloudCompare and the results are shown in colour-coded
distance maps. As a “correspondence metric” we used the
distances between two point clouds computed directly by the
Figure 5. Comparison between meshing systems: (from left to right) model made using Photoscan, Meshlab (decimated after
meshing), and Meshlab (decimated before meshing)
M3C2 CloudCompare plugin (Lague et al. 2013). This
algorithm gives an accurate measurement of the orthogonal
distance between two point clouds, avoiding meshing and
reducing the influence of surface roughness.
The bundle adjustment theory has been around for a long time:
while disregarding the different solutions implemented in a
number of software programs, the principle of bundle block
adjustment is based on the collinearity principle and it is used
for calculating the orientation parameters and generating a
sparse 3D point cloud of a scene.
Photoscan software works as a “black box” since few references
are provided on the implemented algorithms. Hereafter we
present the workflow and provide some details step by step,
according to the available documentation (Agisoft LLC 2014)
and to our experience.
- Photo alignment: this step is performed using image data
alone. Without geometric control information it is possible
to reconstruct the 3D scene, but on an unknown scale, and
with unknown translation and rotation with respect to the
target coordinate system.
- After the GCP insertion, a seven parameter transformation
is performed and the point model is referred to the required
coordinate system.
- An optimization tool is then used to adjust estimated point
coordinates and camera parameters, thus minimizing the
sum of reprojection errors (i.e. the differences between the
measured and the back-projected image points).
9.2 Dense point cloud comparison
All the comparisons presented are computed on raw data, that is
to say on point clouds, in order to avoid the intermediate step of
meshing, which can add computational complexity and
introduce data interpolation.
When the bundle block process is only based on a free network
(without any additional constraints) and GCPs are only used for
a later transformation, the inner accuracy of the image block
might be critical from a geometrical point of view, and the GCP
based transformation does not change the inner geometry.
(Remondino et al. 2012)
Figure 8. Comparison between the (reference) scan data and the
photogrammetric dense cloud – GCPs are only used for
referencing the model – Step 1
9.2.1 Step 1: The image network of our test project
integrated an open sequence of quite normal images (that might
be considered weak geometry) with numerous other convergent
images (Figure 3). It is well known that convergent images
strengthen the network geometry (Nocerino et al. 2014); in our
case they are also indispensable for documenting the
foreshortened parts. Even if we combined 60 normal images and
255 convergent ones, the shape and dimension of the object
affects the results. By comparing the model obtained with the
scan data, a non-linear deformation is evident (see Figure 8).
The shape of the test area is for the most part long and quite
flat: 5,5 m x 1,5 m (high) x 0,3 m (max. depth) and the
photogrammetric results are bent with respect to scan data (the
maximum distances are about ± 2 cm).
9.2.2 Step 2: The Photoscan “Optimize” tool minimizes the
differences between the measured GCPs and the back-projected
image points after the alignment phase. We kept camera
parameters fixed based on the previous calibration. We
expected to obtain an undistorted model, but the non-linear
deformation went back only partially (see Figure 9): it is almost
completely recovered in the middle part but still evident at the
either end.
Figure 9. Comparison between the (reference) scan data and the photogrammetric dense cloud – GCPs are used in an optimization
process (GCPs in white, CPs in red) - Step 2
Figure 10. The same part of the frieze as resulting from the
alignment process of a complete dataset (object size approx.
6 m) and from the alignment of a sub-set of images, relating to a
smaller portion of the object (approx. 2 m) – Step 3
9.2.3 Step 3: Since the results are affected by the shape of
the object and the (related) camera network, we subdivided the
project into smaller parts. An object with more homogeneous
dimensions and a corresponding more suitable camera network
help to avoid deformations: Figure 10 both shows respectively
the same part of the frieze as resulting from the alignment
process of a complete dataset (object size approx. 6 m) and from
the alignment of a sub-set of images, related to a smaller portion
of the object (approx. 2 m). In this model there is a minimal
amount of bending (about ± 2 mm) in the more external parts
only.
9.2.4 Step 4: In a general workflow, Photoscan expects to
optimize approximate inner camera parameters (from EXIF
data) with a self-calibration process or to use fixed values from
a previous calibration.
Figure 11. The outcome of the auto-calibration project
demonstrates a very good conformity with the scan data
assumed as the reference – Step 4
As we stated in 5.1.2, we made a calibration project on a 3D test
field and shot oblique and rotated images in order to decouple
the parameters. The principal distance, the coordinates of
principal point, and radial distortion parameters were computed
using PhotoModeler software. As final step we recomputed the
bundle adjustment with self-calibration. The distances between
the outcome model and our reference data are small and evenly
distributed (see Figure 11), but over-parametrization seems to
occur.
Control Points Check points
RMSE
[mm] RMSE
[pix] RMSE
[mm] RMSE
[pix]
STEP1 9.07 1.60 9.87 1.34
STEP2 7.73 0.09 9.64 0.13
STEP3 6.00 0.15 4.98 0.15
STEP4 5.37 0.08 7.07 0.12
Table 12. Metrics provided by Photoscan
More tests on this topic are underway because Photoscan does
not provide any useful metrics for evaluating the estimation of
the camera model, such as the accuracy of the estimated
parameters, and the correlations among the elements of the
inner and external orientation.
For the ongoing alignment phase we broke down the frieze into
sub-projects and tested bundle adjustment with self-calibration
for each of them: Table 13 shows that the inner parameters are
not stable and some coupling values are evident, even though
all the images were obviously taken with the same camera/lens.
Table 13. Camera inner parameters computed by self-calibration in different sub-projects. Even if all photos have been taken with
the same camera/lens settings, parameters significantly change respect to pre-calibrated values and in different sub-projects.
10. FINAL REMARKS
Nowadays 3D high-resolution models can be produced both
using range-based and image-based techniques (El Hakim et al.
2003). The paper presents the preliminary studies carried out for
the digitization of the earthenware frieze of the Ospedale del
Ceppo in Pistoia (Italy), which is a challenging case study due
to the materials and surface characteristics (Figure 14). Some
tests with different laser scanners and a photogrammetric
project are described. Since we repeated the scans twice
independently and with different instruments, achieving
equivalent models (regardless of the level of detail), a scan data
set was taken as a reference. Several parameters can affect the
results and it is tricky to plan the optimal workflow. The results
obtained by the Photoscan commercial software are discussed.
Despite a camera network defined by a mix of normal and
convergent images, the object’s shape (longer than it is wide)
affects the result, producing a slightly bent model. As is well
known, GCPs are used to reference the model during bundle
adjustment, but in this case they are not able to completely
balance out that deformation. Since it will not be possible to
straighten the camera network due to scaffolding restricting the
shooting positions, the results found suggest subdividing the
frieze into smaller parts; moreover the number of GCPs should
be reduced. The first results obtained by introducing self-
calibration optimization show an undistorted model, according
to the reference scan data. A closer examination of the camera
self-calibration process available in Photoscan is needed, since
it seems to get better results but it might present some problems
of over-parametrization.
ACKNOWLEDGEMENTS
This work was funded by the “Il fregio robbiano dell’Ospedale
del Ceppo: documentazione metrica per il progetto di
conservazione” agreement between the Azienda U.S.L di
Pistoia and the University of Florence – Department of Civil
and Environmental Engineering – DICEA. The authors are
really thankful to arch. Valerio Tesi (Soprintendenza per i Beni
Architettonici, Paesaggistici, Storici, Artistici ed
Etnoantropologici per le province di Firenze, Prato e Pistoia)
and ing. Fabrizio D’Arrigo (Azienda USL3 Pistoia),
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... The IBM pipelines can effectively involve oblique imagery and require low levels of supervision and user expertise, making them extremely popular for digitizing the historic built environment. Many studies report the application of IBM workflows for documenting monumental architecture [98][99][100][101][102][103] (Figure 2) and other historical constructions [104][105][106][107], often supporting the implementation of failure analysis through numerical modeling. In any case, IBM is seldom considered as a stand-alone solution for nondestructive evaluation of historical structures [108][109][110], in all likelihood due to the higher cost-effectiveness of TLS to produce large-volume models suitable for deformation monitoring. ...
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... As a result, these techniques have become widely used in heritage science. Typical applications involve the documentation of archaeological remains (McCarthy 2014; Douglass et al. 2015;López et al. 2016;Toprak et al. 2019), architectural details (Columbu and Verdiani 2014;Tucci et al. 2015;Russo et al. 2019), sculptures (Malik and Guidi 2018;Girelli et al. 2019;Koehl and Fuchs 2019), artifacts (Santos et al. 2017;, implementations to support the integration of multidisciplinary diagnostical data (Adamopoulos et al. 2017;Mandelli et al. 2017), and virtual restorations or reconstructions (Tucci et al. 2017;Chen et al. 2018;Fazio and Lo Brutto 2020). ...
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... As mentioned, digitizing earthenware was the main issue in this project. On the basis of its extensive experience in 3D laser scanning and photogrammetry recording (Grussenmeyer et al., 2016), the INSA Photogrammetry and geomatics Group team saw the key to success in the control of light conditions (Tucci et al. 2015) and the homogenization of light to avoid specular reflections in photogrammetry (Nicolae et al., 2014). Due to technical reasons (large glass walls on both ends of the Winter Garden), it was impossible to regulate or homogenize the amount of light entering the sensor, but the camera chosen (Canon EOS 5DSR) allowed us to take images in RAW format and reprocess them afterwards. ...
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This paper presents the work accomplished in order to digitize and model the Winter Garden of the Earthenware Museum at Sarreguemines (Moselle, France). The objectives were to create a digital archive of this cultural heritage place and to find appropriate ways of promoting it. Topographic, photogrammetric and lasergrammetric methods were used to model the Winter Garden. Different virtual reality tools enabled us to spotlight some of its parts and to offer an immersive visualization. Several challenges had to be taken up during this project. We first had to find out the best processing workflow to model earthenware, which is a highly reflecting material. Image processing was also needed for aesthetic reasons and we finally had to find methods to reduce the size of the models.
... And so it was that during recent restoration of the loggia of the Ospedale del Ceppo in Pistoia (Italy), a survey was commissioned to document the state of the building and to map out the conservation work. The project provided the opportunity to test and also assess other potentials of the acquired 3D data (Tucci et al. 2015a). ...
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... In particular, attention is focused on the possible use of both techniques to produce large-scale orthoimages which can support multidisciplinary analyses and conservation projects. The GeCo Laboratory has recently carried out various 3D surveys on real and complex case studies, including the glazed earthenware frieze at Ospedale del Ceppo in Pistoia (Tucci, 2015) and the Fortezza da Basso in Florence. On all these case studies, it was assessed beforehand whether to choose photogrammetric rather than laser scanning techniques. ...
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The Baptistery of San Giovanni is one of the most important pieces of architecture in Florence. It is an octagonal building, encrusted with marble both internally and externally (including the pyramidal roof) and covered inside by a magnificent dome with sparkling gold mosaics. During Dante’s time, it appeared much older than the other monuments, so its origins were considered as hailing straight from Florence’s most remote and mythical history. Even though we have much more data now, scholars still disagree over the interpretations on the origin and construction sequence of the monument. Survey has always been considered a main instrument for understanding historical architecture, mostly from constructional and structural points of view. During the last century, the Baptistery was surveyed using both traditional techniques and the most up-to-date instruments available at the time, such as topography, close-range photogrammetry and laser scanning. So, a review of those early applications, even if partial or isolated, can significantly attest to the state of the art and evolution of survey techniques. During recent years, the Opera di Santa Maria del Fiore promoted new research and a wide range of diagnostic investigations aimed at acquiring greater knowledge of the monument in anticipation of the cleaning and restoration of the outer wall surfaces during 2015. Among this research, GeCo Lab carried out a new systematic and complete laser scanner survey of the whole Baptistery, acquiring data for the more inaccessible parts that were given little attention during other survey campaigns. First of all, the paper analyses recent contributions given by instrumental surveys in advancing knowledge of the building, with references to the cutting-edge techniques and measurement tools used at the time. Then, it describes the new survey campaign, illustrating the approach followed in the planning, data acquisition and data elaboration phases; finally, it gives examples of some interpretations of the structure stemming from the new acquisitions.
... In particular, attention is focused on the possible use of both techniques to produce large-scale orthoimages which can support multidisciplinary analyses and conservation projects. The GeCo Laboratory has recently carried out various 3D surveys on real and complex case studies, including the glazed earthenware frieze at Ospedale del Ceppo in Pistoia (Tucci, 2015) and the Fortezza da Basso in Florence. On all these case studies, it was assessed beforehand whether to choose photogrammetric rather than laser scanning techniques. ...
Article
Full-text available
The Baptistery of San Giovanni is one of the most important pieces of architecture in Florence. It is an octagonal building, encrusted with marble both internally and externally (including the pyramidal roof) and covered inside by a magnificent dome with sparkling gold mosaics. During Dante’s time, it appeared much older than the other monuments, so its origins were considered as hailing straight from Florence’s most remote and mythical history. Even though we have much more data now, scholars still disagree over the interpretations on the origin and construction sequence of the monument. Survey has always been considered a main instrument for understanding historical architecture, mostly from constructional and structural points of view. During the last century, the Baptistery was surveyed using both traditional techniques and the most up-to-date instruments available at the time, such as topography, close-range photogrammetry and laser scanning. So, a review of those early applications, even if partial or isolated, can significantly attest to the state of the art and evolution of survey techniques. During recent years, the Opera di Santa Maria del Fiore promoted new research and a wide range of diagnostic investigations aimed at acquiring greater knowledge of the monument in anticipation of the cleaning and restoration of the outer wall surfaces during 2015. Among this research, GeCo Lab carried out a new systematic and complete laser scanner survey of the whole Baptistery, acquiring data for the more inaccessible parts that were given little attention during other survey campaigns. First of all, the paper analyses recent contributions given by instrumental surveys in advancing knowledge of the building, with references to the cutting-edge techniques and measurement tools used at the time. Then, it describes the new survey campaign, illustrating the approach followed in the planning, data acquisition and data elaboration phases; finally, it gives examples of some interpretations of the structure stemming from the new acquisitions.
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This paper is a preface of the ISPRS/EuroSDR Workshop on the “Evaluation and Benchmarking of Sensors, Systems and Geospatial Data in Photogrammetry and Remote Sensing” that was held in Warsaw University of Technology on 16-17 September 2019. The paper reviews benchmarking in photogrammetry and remote sensing found in the literature relating to geodata. The first part of the paper is a bibliographic analysis based on queries in Scopus and Web of Science databases which shows an increase in research activities based on benchmarking data. In the second part, a review of past and ongoing benchmarking initiatives is presented, providing examples of initiatives within e.g. ISPRS and EuroSDR. The topic of evaluating data, sensors and algorithms with benchmarking activities is interesting as it provides the opportunity to compare, with a unique approach, research results from independent scientists. As hereafter reported, benchmarking has increased in recent years, with many benchmarks being presented in the photogrammetric and remote sensing communities.
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Tools and algorithms for automated image processing and 3D reconstruction purposes have become more and more available, giving the possibility to process any dataset of unoriented and markerless images. Typically, dense 3D point clouds (or texture 3D polygonal models) are produced at reasonable processing time. In this paper, we evaluate how the radiometric pre-processing of image datasets (particularly in RAW format) can help in improving the performances of state-of-the-art automated image processing tools. Beside a review of common pre-processing methods, an efficient pipeline based on color enhancement, image denoising, RGB to Gray conversion and image content enrichment is presented. The performed tests, partly reported for sake of space, demonstrate how an effective image pre-processing, which considers the entire dataset in analysis, can improve the automated orientation procedure and dense 3D point cloud reconstruction, even in case of poor texture scenarios.
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Conference Paper
Full-text available
In this paper a multisensor approach (topography, photogrammetry, laser scanning) was exploited to generate a close range model of a cultural heritage object in order to evaluate the accuracy of data in all steps of the model production, from the acquisition to the representation. A programmed data redundancy allowed to verify the accuracy of each technique as well as the presence of possible surveying errors. At the end, a comparison from data acquired by different tecniques was done in order to verify the accuracy of the laser model. We have explored the entire workflow to obtain a complete 3d model: range image registration, points decimation, triangulation, mesh editing, model texturing. The results are here presented.
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Within a project for the knowledge and preservation of the mock-up of Giambologna's Ratto delle Sabine housed in the Galleria dell'Accademia in Florence, the GeCO laboratory has made laser scanner acquisitions to create surface models at different resolutions for structural analysis, on which to check the coverage of the photographic campaign and to create a three-dimensional thematic mapping of data relating to investigations and restoration works. The PDF3D file format has been used to easily manage data on a platform immediately available to all operators.
Conference Paper
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The easy generation of 3D geometries (point clouds or polygonal models) with fully automated image-based methods poses nontrivial problems on how to check a posteriori the quality of the achieved results. Clear statements and procedures on how to plan the camera network, execute the survey and use automatic tools to achieve the prefixed requirements are still an open issue. Although such issues had been discussed and solved some years ago, the importance of camera network geometry is today often underestimated or neglected in the cultural heritage field. In this paper different camera network geometries, with normal and convergent images, are analyzed and the accuracy of the produced results are compared to ground truth measurements.
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In order to analyze the potential as well as the limitations of low-cost RPAS photogrammetric systems for architectural cultural heritage reconstruction, some tests were performed by a small RPAS equipped with an ultralight camera. The tests were carried out in a site of remarkable historical interest. A great amount of images were taken with camera’s optical axis in vertical and oblique position. Images were processed by the commercial software PhotoScan of Agisoft and numerous models were realized, each of them was compared with an accurate TLS model used as a reference. The test, despite some problems found, has provided good results in terms of accuracy (average error
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Nowadays, multi-image 3D reconstruction is an active research field and a number of commercial and free software tools have been already made available to the public. These provide methods for the 3D reconstruction of real world objects by matching feature points and retrieving depth information from a set of unordered digital images. This is achieved by exploiting computer vision algorithms such as Structure-From-Motion (SFM) and Dense Multi-View 3D Reconstruction (DMVR). In this work, we evaluate the performance of a low-cost commercial SFM DMVR software by digitising a Cycladic woman figurine. Although the surface properties of the specific artefact are considered 3D laser scanner friendly, its almost featureless white-grey surface composes a challenging digitisation candidate for image based methodologies as no strong feature points are available. We quantify the quality of the 3D data produced by the SFM DMVR software in relation to the data produced by a high accuracy 3D laser scanner in terms of surface deviation and topological errors: We question the applicability and efficiency of two digitisation pipelines (SFM DMVR and laser scanner) in relation to hardware requirements, background knowledge and man-hours. This is achieved by producing a complete 3D digital replica of the Cycladic artefact by following both pipelines.
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Image matching has a history of more than 50 years, with the first experiments performed with analogue procedures for cartographic and mapping purposes. The recent integration of computer vision algorithms and photogrammetric methods is leading to interesting procedures which have increasingly automated the entire image-based 3D modelling process. Image matching is one of the key steps in 3D modelling and mapping. This paper presents a critical review and analysis of four dense image-matching algorithms, available as open-source and commercial software, for the generation of dense point clouds. The eight datasets employed include scenes recorded from terrestrial and aerial blocks, acquired with convergent and normal (parallel axes) images, and with different scales. Geometric analyses are reported in which the point clouds produced with each of the different algorithms are compared with one another and also to ground-truth data.