teration several 
ent weights for 
y. The following 
m of all weights 
> weights of all 
of 3D points is 
nean differences 
1 and maximum 
weights from 
. Table 3 shows 
  
n maximum 
  
2.7354 
  
3.5491 
  
4.4104 
  
  
) 4.8409 
  
  
  
m maximum 
  
  
  
  
  
  
  
9 14.5787 
5 16.8064 
6 19.1585 
8 19.5025 
ce 
  
te surface 
  
Figure 7. Two residual images of concrete surface 
Besides statistical data about the geometry of both surfaces 
given in the tables above, also visualisation of the surfaces, e.g. 
fig. 6, combined with information on point distribution, figures 
4 and 5, and especially residual images, e.g. fig. 7, are valuable 
sources of information on the properties of the approach. 
3.2 Discussion of the results 
A comparison of the results of computations with different 
weights and the reference data given by stripe projection clearly 
shows, that there are only very small changes in Zmean in both 
data sets. Also the largest negative differences (column 
minimum in tables 2 and 3) are almost constant while the largest 
positive differences (maximum) moderately increase when the 
weights of 3D points diminish. Obviously there is almost no 
contradiction between 3D points and the surfaces derived from 
the intensity images. 
The remaining differences which can clearly be seen from 
minimum and maximum in tables 2 and 3 and also from Zmean 
in table 3 seem to be caused by various other reasons. One 
reason is the uneven distribution of 3D points acquired by the 
stripe projecting device. Figure 3 shows dense point clouds at 
the edge of the test surface and gaps in the curved centre. Also 
the concrete surface, cf. fig. 4, shows gaps on the gravel. These 
areas are much larger than the resolution of the surface 
description (1 mm) and, thus, require either supporting image 
data or some kind of regularisation. As the image data does not 
contain sufficient gradients, cf. fig 5, in the same region of the 
surface it does not contribute to the determination of the surface 
parameters. The effect of specular reflection on the object is an 
additional error source. 
First experiments with regularisation by curvature reduction 
with varying weights did not yield satisfying results. Figure 6 
shows a colour coded height model of the surface reconstruction 
revealing gross errors in areas without texture and 3D points. 
Depending on the strategy used for processing (with or without 
hierarchical object model and image pyramid) the influence of 
the approximate values on the final result is difficult to balance 
by choosing appropriate weights. 
Figure 7 finally shows the effect of an unsuitable surface model. 
The large positive and negative residuals (coded black and 
white in the figure) in two out of seven images each lie on parts 
of the surface which are not projected in all digital images and 
leading to contradictions in the reconstruction process. 
4. CONCLUSIONS 
In the presented paper the approach of combined adjustment of 
3D point data and digital photogrammetric images has been 
applied to close range applications for the first time. The results 
are promising under the aspect of mutual support of the 
different data. 
It could clearly be shown that in case of smooth surfaces the 
chosen 2 1/2 D surface model delivers highly precise results and 
an excellent representation of the surface. Larger differences 
between the photogrammetric results and the true surface given 
by stripe projection are most likely caused by faint image signal 
or un-modelled specular reflection on the surface. 
In the case of rough and structured surfaces a refinement of the 
surface model to a true 3D representation seems to be 
unavoidable. 
Further research work still has to be done considering the 
determination of adequate weights for 3D points, intensity 
observations and regularisation functions. 
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GOM 2002: Product information ATOS. 
http://www.gom.com/En/Products/atos vars.html (accessed 15 
June 2002) 
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