The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bib. Beijing 2008 
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oriented scene is about 4 degree and can be reduced to about 
1.5 degree. In addition to a global adjustment, every estimated 
surface plane can be corrected locally. So, for this scenario with 
several façades, only the façade at the front of the street can be 
extracted. Other façades are at least partially occluded and can 
not be estimated correctly. 
5.3 Comparison to other scenarios 
In scenario 2 and 3, the façade is tilted in moving direction 
caused by the small upwards viewing angle of the camera. The 
distance to the building is adequate with about 1 meter and the 
shift is in moving direction is about 4 meters. Both values are 
somehow higher because the GPS coordinates are afflicted with 
a bigger error. This is caused by the record situation. Whereas 
in scenario one, you can find an open park landscape on the 
opposite side of the building, in scenarios two and three, there 
are buildings on the opposite side occluding the satellite signal 
and so leading to a bigger positioning error. For scenario two, 
there is a special case (see Figure 6). The first part of the façade 
has a very regular structure and thus the algorithm finds many 
wrong pairs of points of interest (Fig. 6a). This causes long 
computation steps and wrong planes estimations. This effect 
depends on the points chosen by RANSAC and produces 
changing hypotheses for every computation. A second problem 
of this façade is the gateway at the beginning (Fig. 6b). This 
gateway is standing back from the façade generating points of 
interest behind the surface plane. Together with pillars in front 
of the gateway, which are standing in the surface plane, a point 
cloud is computed with points jumping between the surface 
plane and the gateway plane. In this area, no surface plane can 
be estimated. 
Fig. 6: Scenario two, a) regular façade structure causing wrong 
point pairs, b) gateway behind the façade plane with 
pillars in front. 
In scenario 4 only parts of the façade can be extracted due to 
the occlusion caused by the bridges. The façade parts before 
and after the bridges can be estimated to about 70%. The 
algorithm aborts when the bridges cover more than 40% of the 
image. The positioning errors for the extracted planes are 5 
meters along the façade and 2 meters to the correct façade plane. 
6. DISCUSSION 
Over all scenarios, some facts can be integrated. For all 
scenarios, the upward angle of the camera view • cuased a 
constant tilt of 6 ° in the found surface planes. Façades parallel 
to the moving direction that are not occluded in parts can be 
extracted with about 85 to 90 % when the façade has an almost 
plane structure. The positioning errors are in the range of the 
accuracy of the GPS. The scenario with the bridges can not be 
extracted with appropriate quality, but this is a very special case. 
An overview over the errors in the different scenarios is given 
in table 1. 
Scene 
I 
2 
3 
4 
Pose error model 
X 
3 m 
4 m 
4 m 
5 m 
Y 
Z 
!4 m 
1 m 
1 m 
2 m 
Tilt error 
6° 
6° 
6° 
6° 
Pose error GPS 
3 m 
4 m 
4 m 
5 m 
Angle x-z-plane error 
4 ‘/2° 
4° 
4 1 / 2 ° 
6 0 
Completeness façade 
90% 
85 % 
90% 
40% 
Completeness model 
55 % 
80% 
90% 
40% 
Table 1: Errors in the different scenarios: Pose error model: 
Error of the estimated position to the given model in 
X, Y and Z axis, Tilt error: Tilt of the planes caused 
by upward viewing angle of the camera, Pose error 
GPS: mean error of GPS position to corrected 
estimated position, Angle X-Z plane error: Error in 
the estimated camera path direction, Completeness 
façade: Percentage of completeness of a detected 
façade, Completeness model: Percentage of 
reconstructed façade surface of the hole scene 
including non-detected surfaces 
7. CONCLUSION 
The used algorithm was originally designed for high resolution 
images that show an object from different positions and viewing 
angles. The results show that the 5-point-algorithm can also be 
used for image sequences with constant viewing direction, if 
there are façades parallel to the moving direction. Façades 
observed in nadir view do not show enough movement of points 
of interest to extract surface planes. The images taken for the 
extraction do not need to be given in a high resolution. A small 
resolution as given in the scenarios of this paper often contains 
enough information to extract sufficient points of interest. 
Problems occur if the façade contains only few structure and if 
the structure is regular. When a façade is standing along the 
street with only little occlusion, the façade can be reconstructed 
with up to 90 %. For special façades like mentioned in chapter 
4 the algorithm is not robust enough and reconstructs only small 
parts of the façades. 
The algorithm does not only provide estimated surfaces but also 
an estimated camera path. Results show, that it is possible to 
reduce the position error caused by the GPS by combining the 
GPS and the given 3d model with the estimated surface planes 
and the estimated camera path. The estimated camera path is 
afflicted by a much smaller error than the GPS measurement. 
The GPS position are only necessary for the scaling, rotation 
and translation of the estimated model to fit the given 3d model. 
The position refinement is then done without the measured 
camera path comparing the positions of the façades and the 
surface planes. 
8. OUTLOOK 
Two further steps are now possible: The corrected camera path 
can be used to extract the infrared textures from the image 
sequence. Or, the 3d model surfaces are included in the plane 
estimation process to precise the estimated planes and thus