SfM FOR ORTHOPHOTO GENERATION: A WINNING APPROACH FOR CULTURAL
HERITAGE KNOWLEDGE
F. Chiabrando
a
, E. Donadio
b
, F. Rinaudo
b
a
Dept. of Environment Land and Infrastructure Eingeneering, Politecnico di Torino Corso Duca deglia Abruzzi 24, 10129 Torino,
Italy – filiberto.chiabrando@polito.it
b
Dept. of Architecture and Design, Politecnico di Torino, Viale Mattioli 24, 10125 Torino, Italy-
(elisabetta.donadio,fulvio.rinaudo)@polito.it
Commission VI, WG VI/4
KEY WORDS: Cultural Heritage, close range photogrammetry, RPAS, MicMac, Photoscan, multi-image matching.
ABSTRACT:
3D detailed models derived from digital survey techniques have increasingly developed and focused in many field of application.
The high detailed content and accuracy of such models make them so attractive and usable for large sets of purposes in Cultural
Heritage. The present paper focuses on one of the main techniques used nowadays for Cultural Heritage survey and documentation:
the image matching approach or Structure from Motion (SfM) technique. According to the low cost nature and the rich content of
derivable information, these techniques are extremely strategic in poor available resources sectors such as Cultural Heritage
documentation.
After an overview of the employed algorithms and used approaches of SfM computer vision based techniques, the paper is focused in
a critical analysis of the strategy used by two common employed software: the commercial suite Agisoft Photoscan and the open
source tool MicMac realized by IGN France. The experimental section is focused on the description of applied tests (from RPAS
data to terrestrial acquisitions), purposed to compare different solutions in various featured study cases. Finally, the accuracy
assessment of the achieved products is compared and analyzed according to the strategy employed by the studied software.
1. INTRODUCTION
Dense image matching methods enable the extraction of 3D
point clouds and the generation of 3D models through a
processing of a set of unoriented images acquired from multiple
views. Over the last decade, many algorithms for image
processing techniques in relation to geomatic fields have been
improved. The MSER: Maximally Stable Extremal Regions,
SIFT: Scale Invariant Feature Transform (Lowe, 2004), SURF:
Speed Up Robust Feature (Bay et al., 2006) are the most
important algorithms that have given a renovation interest in
digital photogrammetry to the detriment of LiDAR technique
(always expensive and not very widespread).
Nowadays the image matching problem can be solved using
stereopairs (stereomatching) (Hirschmuller, 2011) or via
identification of correspondences in multiple images (multi-
view stereo – MVS) (Pierrot-Deseilligny and Paparoditis,
2006). As explained by (Remondino et al., 2014), according to
(Szeliski, 2010), stereo methods can be local or global. Local
methods use the intensity values within a finite region to
compute disparity at a given point, with implicit smoothing
assumptions and a local “winner-take-all” optimization at each
pixel, whereas global methods, making explicit smoothness
assumptions, solve for a global optimization problem using an
energy minimization approach.
The great innovation in the image matching process related to
photogrammetry techniques consists in the implementation of
the Structure from Motion (SfM) technique. While traditional
photogrammetry derives calibration parameters of the camera
and the camera poses mainly from well-distributed GCPs and
tie points, a Structure from Motion (SfM) approach computes
simultaneously both this relative projection geometry and a set
of sparse 3D points. To do this, it extracts corresponding image
features from a series of overlapping photographs captured by a
camera moving around the scene (Verhoeven et al, 2012).
This image-matching methodology was developed and tested
firstly for Remote Sensed data. At first, it has been planned to
meet orientation solutions and then to perform DTM/DSM
(Digital Terrain Model / Digital Surface Model) extraction from
aerial or satellite strips; more recently, it is extensively used in
close-range application concerning architectural and
archaeological survey. It is well accepted that the tie points (TPs)
searching is simpler working on traditional aerial strips than
using close range ones, because of the major variance in
geometry and radiometry of terrestrial acquisition.
Currently, the algorithms for retrieval of 3D information are
primarily based on computer vision methods and they can be
separated into two categories (Wenzel et al., 2013). The first
category retrieves image orientation parameters determining,
with manual or automatic methods, distinct features in the
images, followed by bundle adjustment. The second category
represents surface reconstruction methods, where dense image
matching algorithms exploit the previously derived orientation
of the images to derive complete surface. These techniques
allow the generation of 3D information even if the images are
acquired by non-expert people in the field of Photogrammetry
and 3D reconstruction (Pierrot-Deseilligny et al., 2011).
In this scenario, it is important to underline the ability to extract
from such data section planes in sensitive zones of the building,
for bi-dimensional representation, or the possibility to generate
3D representation emphasizing diverse phenomena (wireframe,
shaded, digital elevation models). The models achievable from
this data processing are very useful for CH valorization, for the
specialists web sharing and for spreading knowledge to a larger
public.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5/W7, 2015
25th International CIPA Symposium 2015, 31 August – 04 September 2015, Taipei, Taiwan
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-5-W7-91-2015
91
The coordination of multidisciplinary sectors is under great
attention, since the management of such detailed and flexible
models in web-GIS systems is nowadays increasing in the field
of CH (Krooks, et al., 2014; Pal Singh et al., 2014).
2. FROM DIGITAL PHOTOGRAMMETRY TO SfM
The chance to derive 3D information from images is strictly
connected with the ability to pick out corresponding points in
images shooting the same object from different positions. In
analogical and analytical photogrammetry this action has always
been performed manually, while with the advent of digital
photogrammetry, many reasons and benefits have encouraged
the semiautomatic and automatic procedure. Starting from this
assumption, after a first revolution phase that involved the
transition from analytical to digital, improving an automation of
photogrammetric process (point extraction, orientation digital
plotting etc), today we are assisting to a second revolution. This
revolution is pushing digital photogrammetry (semi-automatic
oriented) to the Structure from Motion approach, naturally
related to photogrammetric basis (measures, accuracy etc) but
very close to the computer vision approach: fully automatic
with a measurement approach not very important. After an
initial enthusiasm, which usually occur with new trends, a
deeper analysis on the real potentiality for CH documentation of
these techniques is today needed.
On the other hand, it is clearly admitted that these techniques
allow everyone to do photogrammetry; this was one of the main
objective of the researcher involved in this area.
The improvement is evidently connected to the algorithms
development. Such algorithms
are used in a wide variety of
applications but were developed in the 1990s in the field of
computer vision, which is the science that develops
mathematical techniques to recover a variety of spatial and
structural information from images.
Structure from Motion allows the generation of 3D data from a
series of overlapping images, employing same basic tenets as
stereoscopic photogrammetry. However, it differs from
conventional photogrammetry, since camera pose and scene
geometry are reconstructed simultaneously using a highly
redundant, iterative bundle adjustment procedure. This process
works through the automatic identification of matching
featuresin multiple images without requiring the specification a
priori of a network of targets...
Such features are tracked among all images and then refined
iteratively using non-linear least-squares minimization, enabling
initial estimations of camera positions and object coordinates. It
is important to underline that this approach is most suited to
sets of images with a high degree of overlap that captures full
three-dimensional structure of the scene viewed from a wide
array of positions.
The afore mentioned SIFT (Scale Invariant Feature Transform)
algorithm, developed by Lowe in 2004 (Lowe, 2004), allows
the extraction of such feature points (Figure 1) in four steps:
scale-space extrema detection, keypoint localization, orientation
assignment and keypoint descriptor. In the first stage, it uses the
difference of Gaussian function to identify potential points of
interest; naturally according to the algorithm this points are
invariant to scale and orientation. Difference of Gaussian is
used instead of Gaussian to improve the computation speed.
The low contrast points are rejected and the edge response are
eliminated during the keypoint localization step. The Hessian
matrix is used to compute the principal curvatures and eliminate
the key points that have a ratio between the principal curvatures
greater than the ratio. An orientation histogram was formed
from the gradient orientations of sample points within a region
around the keypoint in order to get an orientation assignment
(Lowe, 2004 ; Ke and Sukthankar, 2004).
Figure 1 Visualization of the extracted TPs in two overlapped
images (Agisoft Photoscan above, MicMac below)
Sometimes SIFT data processing is quite slow (Lingua et al.,
2009), reason why the research is now focusing on improving
the speed of the algorithms even more. In 2006, Bay, Tuytelaars
and Van Gool published the paper: SURF: Speeded Up Robust
Features, which introduced a new algorithm called SURF (Bay
et al., 2006). As the name suggests, it is a speeded-up version of
SIFT. In SIFT, Lowe approximated Laplacian of Gaussian (LoG)
with Difference of Gaussian for finding scale-space. SURF goes
a little further and approximates LoG with a box filter.. One big
advantage of this approximation is that, convolution with box
filter can be easily calculated with the help of integral images
and it can be done in parallel for different scales. The SURF
also relies on determinant of Hessian matrix for both scale and
location. For orientation assignment, SURF uses wavelet
responses in horizontal and vertical direction for a
neighborhood of size 6 pixel; adequate Gaussian weights are
also applied to it. For feature description, SURF uses wavelet
responses in horizontal and vertical direction (again, use of
integral images makes things easier) as well. A neighborhood of
size 20 x 20 pixel is taken around the key point, it is divided
into 4x4 pixel sub-regions and for each sub-region, horizontal
and vertical wavelet responses are taken. Another important
improvement is the use of sign of Laplacian (trace of Hessian
Matrix) for underlying interest point. The sign of the Laplacian
distinguishes bright blobs on dark backgrounds from the reverse
situation. In the matching stage, we only compare features if
they have the same type of contrast. This minimal information
allows for faster matching, without reducing the descriptor’s
performance. Summarizing SURF adds a lot of features to
improve the speed in every step. Analysis shows it is 3 times
faster than SIFT, while performance is comparable to SIFT.
SURF is good at handling images with blurring and rotation,
but not good at handling viewpoint change and illumination
change. Nowadays the principal commercial and non-
commercial software are based on SIFT (Bundler, PMVS) or on
the modified version of SIFT (MicMac, Photoscan, 3DF
ZephyrPro,) in the first part of the workflow. After this phase a
bundle block adjustment (MicMac) or a similarity
transformation (Photoscan) is performed and finally the dense
matching is computed. Such software solutions use multi-view
stereo (MVS) algorithms to generate 3D dense representation of
the object’s surface geometry (Verhoeven, 2012). This
additional step enables the generation of detailed three-
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5/W7, 2015
25th International CIPA Symposium 2015, 31 August – 04 September 2015, Taipei, Taiwan
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-5-W7-91-2015
92
dimensional point clouds or triangular meshes, since MVS
solutions operate on the pixel values instead of on the feature
points (Seitz et al., 2006).
Another interesting approach is the semi global matching (SGM)
algorithm, which was implemented by Hirschmuller
(Hirschmuller, 2011), firstly in aerial application. This approach
combines both global and local stereo methods for an accurate,
pixel-wise matching with higher stability (Wenzel et al., 2013).
While other global matching methods suffer from high
computational efforts, SGM ensures efficient implementations
at low runtime. (Wenzel et al., 2013).
It works computing a disparity map for each pair and then
merging disparity maps sharing the same reference view into a
unique final point cloud. Within a premodule, a network
analysis and selection of suitable image pairs for the
reconstruction process is performed. Epipolar images are then
generated and a time and memory efficient SGM algorithm is
applied to produce depth maps. All these maps are then
converted in 3D coordinates using a fusion method based on
geometric constraints that both help in reducing the number
outliers and increase precision. This is particular successfully
for repetitive or low textured images. In such areas, SMG is still
able to retrieve reliable results. (Remondino et al., 2014).
Starting from this scenario, several tests on different datasets
were performed on UAV and terrestrial images in order to
deeply understand the characteristic of two widely employed
software: Photoscan and MicMac.
The processing steps were analyzed in order to understand the
differences between such software and a typical
photogrammetric approach (starting from the calibration up to
the Orthophoto generation).
3. DATA PROCESSING STRATEGY AND RELATED
PRODUCT. AN OVERVIEW OF THE EMPLOYED
SOFTWARE
In this study, the images were processed using two different
well known software tools: the commercial low-cost software
Photoscan by AgiSoft LLC, and the open-source suite Apero –
MicMac implemented by IGN (Istitut Geographique National)
France.
Photoscan is an advanced image-based solution produced by the
Russian-based company AgiSoft LLC for creating professional
quality three-dimensional (3D) content from still images. This
program has a simple interface and it enables the generation of
sparse, dense point cloud, accurate three-dimensional textured
meshes and other representations such as DSMs and
orthophotos (Verhoeven, 2011). Built to operate on Windows
systems but available on Linux and OS as well, Photoscan can
handle a multitude of JPEG, TIFF, PNG, BMP or MPO files to
generate three-dimensional data. The reconstruction process is
composed by three simple steps, in which the user can set a
large number of input parameters and, at any stage,
disable/enable individual photographs, mask parts of the images
or import textures and meshes created in other applications. The
only assumption for a good reconstruction is that the scene to be
reconstructed is visible on at least two photographs.
How mentioned before, in the first step of the process SfM
technique enables the images alignment, calibration and the
reconstruction of three-dimensional scene geometry and camera
motion. To do this, the program detects image feature points (i.e.
geometrical similarities such as object edges or other specific
details) using an approach similar to the mentioned SIFT
algorithm (a modification of the Lowe algorithm, since this is
protected by the copyright) and, subsequently, it monitors the
movement of those points throughout the sequence of multiple
images. Each point has its own local descriptor, based on its
local neighbor-hood, which is subsequently used to detect point
correspondences across the complete image set (G. Verhoeven
et al., 2012). To perform this step, robust methods such as a
modified version of RANSAC are used.
After this phase, the camera interior and exterior parameters, its
positions and assets are defined in a local reference system. The
interior orientation (focal length, principal point location as
well as three radial and two tangential distortion coefficients) is
computed basing on a radial model and the relative orientation
(Azarbayejani and Pentland, 1995).
The resulting data is a sparse 3D point cloud corresponding to
the locations of the estimated feature points.
In a second step, a dense, multiview stereo reconstruction on
the aligned images is applied, in order to build geometric scene
details. In this phase, the dense reconstruction algorithm works
on the pixel values in order to generate detailed 3D meshed
models.
In this phase, Photoscan allows users to choose among several
dense stereo-matching algorithms (Exact, Smooth, Height Field
and Fast), which differ in the way in which the individual depth
maps are merged into the final digital model (G. Verhoeven et
al., 2012). The final calculated model is equivalent to a digital
surface model (DSM): a numerical representation of the
morphology and its overlying objects. As well known since
conventional orthorectification, such model is essential to
generate true orthophotos, a bi-dimensional representation in
which all objects with a certain height (such as houses, towers
and trees) are accurately positioned and measurable. The
computed mesh can be, finally, textured with the photographs.
Using Photoscan it is possible to set only few parameters
regarding the generation of the first alignment, the dense cloud
and the texture. With the exception of the alignment, that has
been set up at a medium range, all other steps of the workflow
have been set up at the “high” input, that means that the
algorithm extracts a point for each two pixel to generate the
dense cloud.
Furthermore, it is important to highlight that according to the
standard procedure the results are expressed in a local
coordinate framework (that derives from the relative
orientation). Since the applications connected to geomatic
techniques and Cultural Heritage Survey require data with a
defined coordinate system, Photoscan allows to set a coordinate
system based on traditional ground control point (GCPs)
coordinates or, when available, on camera position and attitude
(the latter very useful and common using aerial data where the
acquisition is connected to GNSS and an IMU).
The approach of Photoscan in this part of the data processing
allows to define a simple affine transformation to the final
model in order to minimize the error or using the camera
alignment optimization based on camera or GCP coordinates to
fix non-linear distortions of point cloud model (the so called
blow effect, Figure 2). In this step, probably Photoscan
performs an adjustment based on Gauss-Markov linear model.
This approach differs from the standard aerial photogrammetric
approach, in which georeferencing - which is achieved by the
traditional Bundle Block Adjustment (BBA), sometimes
assisted by data from a GNSS IMU system used for direct
photogrammetry (Jacobsen 2004) precedes the 3D model
generation.
This aspect is very important and lead the user to accurately
check the final results in order to control that any distortion
does not remain in the final 3D model.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5/W7, 2015
25th International CIPA Symposium 2015, 31 August – 04 September 2015, Taipei, Taiwan
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-5-W7-91-2015
93
Figure 2. A view of the blow effect
A different approach is performed by MicMac, which is a
simplified software derived from the original implementation of
Apero (Pierrot-Deseilligny and Paparoditis 2006).
Using this approach the surface measurement and
reconstruction is formulated as an energy function minimization
problem, using a pyramidal processing (Remondino et al.,
2013). This strategy that could be defined as hierarchical is
followed in order to optimize the results in terms of speed and
quality; first the best homologous points are founded using
highly subsampled set of images that allow to product rough
layout data that can be refined step-by-step on images with
gradually improving the resolution (pyramidal approach) and
moreover enables a reduction of the research area for each
pixel. As a result in the workflow, each pyramid level guides the
matching at the next, higher resolution, level in order to
improve the quality of the matching.
After the first points extraction MicMac allow to use a global
method in order to process the entire surface all at once
naturally with the disadvantage of the needed time for data
processing. In order to optimize this process the developer of
MicMac follow the approach of the dynamic programming and
the graph cutting methods. These methods consist in looking for
the minimum of an energy function made up of one part
controlling the similarity between images and another part for
the surface regularization to be reconstructed.
Traditionally MicMac allows the user to choose between two
different processing strategies, called GeomImage and Ortho. In
the GeomImage, the user selects a set of master images for the
correlation procedure; then for each candidate 3D point a patch
in the master image is identified and projected to all the
neighboring images, and a global similarity is derived. Starting
from the latest release of MicMac (April 2015) the GeomImage
strategy has been improved with the new tool C3DC (QuickMap
option) that improves the automation of the complete workflow.
In particular the masking strategy has been improved including
the possibility of making a 3D mask on the point cloud in order
to speed up this part of the process.
Finally using TiPunch and Tequila the mesh using the well
known Poisson algorithm (Kazhdan, et al 2006) and the texture
could be generated as well.
On the other hand in the Ortho strategy, a voxel is defined
according to the block size and camera-to-object distance; then
every candidate 3D point is back-projected onto images and
global similarity is derived.
Summarizing the pipeline of MicMac firstly consists in the tie-
point extractions (Tapioca). In this first step a modified version
of the SIFT algorithm is used for the computation of the
TiePoints (Pierrot-Deseilligny and Cléry, 2011).
After this step the orientation and the camera parameters are
computed. In this part two main different strategy could be
followed in order to obtain a correct survey (with known
dimensions). The simplified strategy after the relative
orientation and camera calibration using Tapas allow to set-up
the scale and an orientation to the object in order to transform
the results from image coordinate to the real word using
Bascule.
The second strategy is more oriented to the photogrammetric
approach and allow to perform a traditional BBA (Campari)
using the ground control points or pose centre coordinates
(often employed in aerial photogrammetry) (Chiabrando et al.,
2014). In the performed tests this second strategy has been
followed.
Subsequently, a dense image matching for surface
reconstruction is realized using a tool called Malt. The dense
DSM is achieved starting from the derived camera poses and
multi-stereo correlation results. Each pixel of the master image
is projected in object space according to the image orientation
parameters and the associated depth values. For each 3D point a
RGB attribute from the master image is assigned (Pierrot-
Deseilligny et al., 2011). Finally the single true orthoimages are
generated using the same tool. After these step in order to
achieve some final products an orthophoto mosaic using Tawny
or a complete point cloud using Nuage2Ply could be generated
as output (Mouget and. Lucet, 2014).
3.1. Orthophoto and Cultural Heritage documentation
Thanks to the above-mentioned advances in the fields of
computer vision and photogrammetry, as well as the
improvements in processing power, it is currently possible to
generate true orthophotos of large, almost randomly collected
aerial photographs in an increasingly automatic way (G.
Verhoeven et al., 2012).
The orthophoto is a very useful product for Cultural Heritage
documentation since in this metric product is possible to
combine radiometric information with real measure allowing a
complete representation from every point of view (both
terrestrial and aerial) of the analyzed object. Moreover, from the
point of view of the actors involved in the restoration or
requalification project this is a fundamental support for
mapping materials, deteriorations or other important effects that
damage a CH under investigation (Koska, et al., 2013;Rijsdijk,
2014).
Finally, using the achieved orthophoto it is possible to integrate
traditional drawings with more descriptive information, also
using this data as texture for virtual reality based application
and 3D modeling purpose.
Today all the software based on matching approach allow to
quickly and easily generate orthophotos but an accurate check is
always necessary in order to understand their final real accuracy.
To do this it is necessary to use several points not employed for
image orientation and adjustment in the matching software.
In order to check the accuracy of orthophotos generated by the
two used software, some tests were realized on three different
data set that cover the main areas of application for Cultural
Heritage documentation. The case study are constituted by
aerial data, by UAV, and close range data at different scale
(from façade, vault and ceiling to object acquired from short
distances).
In the next experimental section, the achieved test and the
achieved accuracy are reported.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5/W7, 2015
25th International CIPA Symposium 2015, 31 August – 04 September 2015, Taipei, Taiwan
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-5-W7-91-2015
94
4. EXPERIMENTAL SECTION
4.1. The hall of honour of the Stupinigi
The first test was carried out on the vault of the hall of honour
of the Stupinigi royal estate (TO, Italy), realized by the architect
Filippo Juvarra for the Royal House of Savoy as a country
residence for hunting from 1729 onwards (Figure 3).
Figure 3. The Stupinigi royal estate (in the circle the royal hall)
The hall of honor was the meeting point for hunting expeditions
and it was also used for royal ceremonies. It is composed of an
oval-based two-floor cylinder, closed by a vault composed of a
rib vault in the centre and four bowl-shaped vaults linked
together by plane surfaces and arcs. The hall is decorated with
frescoes in trompe-l’oeil technique painted architectural frame.
Moreover, most of the architectural elements in the hall
(columns, capitals, friezes, and so on) are not sculpted but the
relief is painted onto a smooth, plastered surface. Valeriani
brothers from Venice under the direction of the architect,
scenographer Filippo Juvarra, painted frescoes.
In the hall detailed metric surveys were carried out with a laser
scanner clouds processing and orthophoto applications obtained
by digital photogrammetry algorithms. The various data were
processed in a unique, local coordinate system using a reference
network of 9 points situated in the hall, partly at ground level
and partly on the balcony on the first floor. Traditional high-
precision total stations were used with redundant and reliable
schema of traditional topographic measurements and the
network was adjusted using the least squares method in order to
reduce instrumental residuals and to control accidental errors.
These points were used as the reference for measuring all the
Ground Control Point coordinates, both through the positioning
of targets and by collimating the natural points on the
decorations.
High resolution photogrammetric images were acquired of the
decorations and decorated surfaces in order to obtain a large
scale model of the decoration details. For this purpose, a
calibrated photogrammetric Canon EOS-1Ds Mark II camera
with the following characteristic was used: Pixel size 7.2 x 7.2
m, sensor size 24x 36 mm, equipped with a 20 mm focal lens.
The vault system was acquired by means of 19 nadir images
from scaffolding about 8 meters above the ground floor
arranged in the shape of a cross along the two axis of the hall.
They overlap each other by about 80-90% and most of the
surface is included in more than 9 images.
Since it was impossible to place some targets directly on the
vault, some natural points, identified on the decoration
drawings, were measured using topographic instrumentation, in
order to reference the processing products to the local
coordinate system of the whole object.
The images of the vault were processed using the two different
software tools, naturally after the orientation phase some control
points have been introduced in all the images in order to
orientate the model in the same coordinate system and estimate
the accuracy of the final output.
A strict selection was performed on used GCPs on the vault
since they were natural points identified from the details of the
frescoes at ground level. The level of accuracy achievable in
these conditions, without targets and shaded drawings as
reference points, and their level of accuracy was not optimal.
This problem was solved by measuring superabundant GCPs in
order to be able to select the best ones. After this, the
processing can be run again to obtain the optimization of the
orientation in Photoscan and the BBA in MicMac and finally to
extract the final products.
In MicMac the first step has been the computation of tie points
(TPs) from all pairs of images, the second step has been the
external orientation (with the camera calibration), following
which a complete bundle block adjustment has been carried out
using GCPs. Finally, multi image matching has been performed
to generate the dense DSM. The last step has been the
generation of the true orthophoto mosaic and the realization of
the point cloud .
Table 1 shows the synthetic results of Photoscan and MicMac
processing.
Photoscan
MicMac
Number of images
19
19
Pose Distance
14.412 m
14.412 m
GSD
4.4 mm/pix
4.4 mm/pix
Coverage area
385.1 mq
385.1 mq
Tie points
119951
130029
Extracted points
4572658
4294953
Table 1 - Results of Photoscan (high settings) and MicMac
model reconstruction processing
Finally starting from these points the DSM and the orthophoto
of the vault was achieved. In the following figure 4 an achieved
orthophoto with 2D drawing and contours is reported.
Figure 4. Orthophoto integrated in a 2D representation with
contours (c)
4.2. The frieze of the Roman arch of Augusto in Susa
A second test case consisted in the photogrammetric survey of
the frieze of the Roman Arch of Susa (Figure 5).
The city of Susa was founded in the first century BC by Celtic
Tribes, which subsequently made an alliance with Roman
people. For these reasons, many Romans remains are still
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5/W7, 2015
25th International CIPA Symposium 2015, 31 August – 04 September 2015, Taipei, Taiwan
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-5-W7-91-2015
95