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MULTI-IMAGE MATCHING: AN “OLD AND NEW” PHOTOGRAMMETRIC ANSWER 
TO LIDAR TECHNIQUES 
F. Nex a *, F. Rinaudo a 
a Dept, of Land, Environment and Geo-engineering (DITAG), Politecnico di Torino, Corso Duca degli Abruzzi 24, 
Torino, Italy, (francesco.nex, fulvio.rinaudo)@polito.it 
Commission V, Working Group V/3 
KEY WORDS: Close Range Photogrammetry, Digital, Matching, Segmentation, Software, Accuracy, Cultural Heritage 
ABSTRACT: 
Over the last decade, LIDAR techniques have replaced traditional photogrammetric techniques in many applications because of 
their speed in point cloud generation. However, these laser scanning techniques have non-negligible limits and, for this reason, 
many researchers have decided to focus on improving the performances of matching technique in order to generate dense point 
clouds from images.The first tests carried out at the Politecnico di Torino on the first fully-automated multi-image matching 
commercial software, the ZScan Menci Software, are described in this paper. This instrument 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 in order to elaborate triplets of oriented aerial images and 
generate DSM: the first results obtained in this way are presented in this paper. Several tests have been performed on the ZScan 
performances analysing different geometrical configurations (base-to-height ratio) and textures. The evaluation of the geometric 
precision obtained by this software in point cloud generation may help to understand which performances can be achieved with a 
fully automated multi-image matching. The evaluation concerns what the most critical aspects of these techniques are and what 
improvements will be possible in the future. Furthermore a possible new research project is described which has the aim of 
transferring useful information about breakline location from images to point clouds in order to derive automatically the 
segmentation algorithms. 
1. INTRODUCTION 
Over the last decade, LIDAR techniques have replaced 
traditional photogrammetric techniques in many applications, 
because of their speed in point cloud generation and the 
percentage of acceptable points; traditional image matching, 
based on the use of stereopairs, can acquire no more than 80% 
of the possible points (the holes have to be manually 
completed). 
However, these laser scanning techniques have non-negligible 
limits due to the impossibility of directly obtaining radiometric 
information and the exact position of object breaklines. For this 
reason, most LIDAR surveys are integrated by digital image 
acquisition. Digital photogrammetry directly associates a 
radiometric information to the acquired points and the use of a 
stereopair allows a manual survey of the breaklines when 
automatic algorithms fail. Then, other limits for LIDAR 
terrestrial applications are the weight and size of the 
instruments, the limited range of applications (especially for 
environmental applications) and last but not least the cost. 
Finally, when point clouds are produced, segmentation and 
classification procedures have to be applied in order to correctly 
interpret and model the surveyed object. The acquired 
experience shows that automatic algorithms are not ready to 
offer correct solutions without direct human help or without an 
image interpretation. 
For these reasons, many researchers have decided to focus on 
improving the performances of matching techniques in order to 
generate dense point clouds from only images. 
2. MATCHING ALGORITHMS 
As is well known, these techniques were devised more than 
twenty years ago and are nowadays subdivided into Area Based 
Matching (ABM) and Feature Based Matching (FBM). 
Since then, the improvement of these techniques has been 
ongoing with research into new algorithms that are capable of 
improving their efficiency and precision. This research has led 
to matching algorithms which have become more complete 
(Gruen, 1985) and improved their effectiveness, imposing new 
constraints, such as collinearity conditions (Gruen, Baltsavias, 
1988), and integrating the surface reconstruction into the 
process. Furthermore, these techniques now allow more than 
two images to be handled and simultaneous determination of all 
matches of a point: these features help overcome the problems 
of mismatches thus increasing the precision achieved (Forstner, 
1998). 
These techniques have recently been improved by other authors 
who have adapted the algorithms to aerial (Gruen, Zhang, 2004) 
and terrestrial photogrammetry (El-Hakim, et al.2007; 
Remondino, 2007), partly overcoming some of the matching 
problems (wide baselines between images, illumination changes, 
repeated pattern and occlusions). An important aspect of these 
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