The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
new algorithms is the integration of photogrammetric equations 
with other devices commonly used in the Computer Vision 
application field such as rectification (Zhang et al., 2003; Du, et 
al., 2004) and tensorial notations (Roncella et al., 2005; Hartley, 
et al., 2000). The practicality of these instruments is essentially 
that they simplify and speed up the photogrammetric process or 
provide traditional techniques with information obtained using 
new techniques such as the shape from shading (Fassold et al., 
2004) where the texture is not sufficient for matching 
techniques. 
Thanks to these algorithms, over the last decade some 3D 
modelling software packages have been created: these new 
instruments can be divided into semi-automated and automated 
software. 
Other papers (El-Hakim et al., 2007) have already described the 
performances of human interactive (semi-automated) software 
and demonstrated the great potential of these techniques. 
The main aim of this paper is to describe the tests that have 
been carried at the Politecnico di Torino on the new Menci 
Software ZScan, one of the first fully automated multi-image 
matching commercial software programmes. This software was 
first devised to allow inexperienced users to generate very 
dense point clouds from image triplets; a customized calibrated 
bar (0,90 m length) is used for image acquisition. Recently, a 
new version of ZScan has been created to elaborate triplets of 
oriented aerial images and generate DSM. 
Some tests are described in the following sections with the aim 
of demonstrating the advantages of using a multi-image 
matching approach compared with traditional stereopair 
management, using ZScan software both for terrestrial and 
aerial applications. 
The principal goal of these tests was to understand whether a 
multi-image approach could produce a point cloud with the 
same precision and density as LIDAR point clouds. 
3. POINT CLOUDS MANAGEMENT 
Once a point cloud has been acquired, many automated and 
manual interventions have to be applied in order to segment, 
classify and model the surveyed points. The main topic is the 
extraction of the breaklines. 
Different ways of solving this problem have already been 
proposed in scientific literature. In photogrammetry one way of 
solving this problem is represented by the extraction of edges 
from images and then their matching in the space using 
different algorithms (Hauel, et al., 2001; Zhang, 2005). 
A segmentation in LIDAR applications has instead only been 
performed using the point cloud information as curvature 
(Beinat et al., 2007) or static moment (Roggero, 2002). 
However practical experience has shown that the use of images 
can be of help for breakline extraction, so all LIDAR surveys 
are usually integrated by digital images recorded during LIDAR 
acquisition. If the images are oriented in the LIDAR coordinate 
system, it is possible to integrate LIDAR point clouds and 
derive automatic segmentation algorithms to find the breaklines. 
If point clouds are generated by means of a photogrammetric 
approach the breaklines can be directly plotted from 
stereomodels. 
A possible research project is described in the last section with 
the aim of transferring useful information about breakline 
location from images to point clouds in order to derive the 
segmentation algorithms automatically. 
4. ZSCAN SYSTEM 
The ZScan system was originally thought up to allow 
inexperienced users to easily generate point clouds from image 
triplets, especially in architectural and cultural heritage surveys. 
In order to reach this goal, ZScan Software needs to have three 
images acquired using a 0.9 m long calibrated bar. The 
maximum baseline between adjacent images is 0.45 m. 
The image acquisition is performed acquiring each image and 
then translating the camera to the following position (figure 1). 
Finally, the acquired images are processed by the ZScan 
Software, just defining the area of interest on the images and 
the dense matching step. The computational times are usually 
moderate as ZScan takes about 30-45 minutes per image triplet 
with a matching step of 3 pixels. 
Figure 1. The ZScan calibrated bar 
In our tests, a Canon EOS 5D camera was used as it guarantees 
good image definition. The technical characteristics of this 
camera are reported in table 1. 
The ZScan software has been modified to also manage aerial 
images and, in a more general approach, each kind of 
normalized triplet of images. The absence of a y-parallaxes 
constitutes a constraint for the correct use of the software. It is 
well known that the relative orientation and the normalization 
of a set of images is an autonomous procedure in digital 
photogrammetry. 
Camera 
f 
[mm] 
Pixel dim. 
[mm| 
Image dim. 
[pixel x pixel] 
Canon EOS 5D 
25,10 
0,0082 
4368 x2912 
Table 1. Technical characteristics of the camera