s for the first image
ctors. For selected
manually to see the
es running out of the
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' homologous points
ner points and SIFT
alucaltion of trifocal
e bundle adjustment
the position of the
ited image sequence
iologous points. The
es and the estimated
1ence can be seen in
ady visible. Most of
indows and grouped
le adjustment of the
shows smaller errors
ints due to the weak
window regions for
ce along a group of
estimated camera
sequence orientation
ig model (Fig. 4). A
matching to remove
trees. The local
derive an estimated
point is assigned to
ind similar normal
direction that differs
cted. The remaining
matching with the
Additionally, a line matching (Frueh et al, 2004) in the image
space is done to refine the exterior orientation of every image
(Fig. 5).
Figure 4. Grid model of the building with point cloud (light
grey) and camera path: light grey: GPS path, dark grey
transformed estimated camera positions
Figure 5. Image overlayed with the grid model of the building.
Left: before line matching, Right: after line matching
3.4 Comparison of extracted Textures
For every surface of the model, partial textures are generated
one from every image where the surface is visible. These
textures normally do not cover the hole surface (Fig. 6). Due to
the recording configuration, the geometric resolution decreases
to the roof and the right and shows only a small part of the
facade on the left.
0000000
Figure 6. Partial texture of one facade extracted from one IR
image
In a first step, the partial textures of one surface from one
sequence are combined (Fig. 7). The resulting combined
textures show a good fitting in the middle of the images but
disturbances at the roof and especially on the ground. The roof
disturbances seem to be caused by the viewing angle and the
low resolution in all images, the errors near the ground are
caused by occlusion.
Figure 7. Surface texture generated from the complete image
sequence
If a texture is associated with a building surface, we are able to
compare different textures from different recording times and
conditions. A straight forward way is to overlay these textures
(Fig. 8). In this example two infrared textures, one from a
sequence in the evening and one from a sequence in the next
morning, are combined. The blue color indicated a cooling
effect over night. One can see, that the position of the windows
in the first and second floor are fitting very well, whereas as the
third floor seems to be blurred. One can also see small diagonal
lines. These lines are the result of the combination of partial
textures of one sequence. This combination are not exact the
same for to textures from different sequences and thus can show
small differences in the intensity.
Figure 8. Temperature differences of two textures from
different image sequences.
4. DISCUSSION AND CONCLUSION
The matching of the point cloud and the building model shows
different behaviour depending on the building geometry. In
many cases, occlusions reduce the number of visible facades in
the image sequence and thus no 3d points for the matching
exists. This sometime leads to remaining shifts of the point
clouds in the facades. These shifts can be reduced by checking
the edges of the building model against the images using
projected voxels of the facade borders. It can be seen, that this
method allows to handle geometric details on the facade which
have not been modelled in the 3d building model itself. The
geometry of the recording leads to an quite unbalanced
distribution of feature points compared to the model surfaces.