u. an 
Figure 5. Extract from RCD30 RGB imagery showing the 
identical location as Figure 6 (mapping vehicle was driving in 
the middle lane with manholes visible on either side). 
  
Figure 6. Mono client with ground-based imagery, and overlaid 
3d vector data (left), map interface (bottom right) and feature 
editor (top right) 
6.3 3d point measurement accuracy comparison 
In order to get a first assessment of the interactive 3d point 
measurement accuracies within the ground-based and the 
airborne stereo imagery, 40 well-defined road markings were 
interactively measured in both data sets. Based on earlier 
investigations and experience, the a priori point measurement 
accuracy should be in the range of: 
e approx. 3-4 cm in X and Y and 2-3 cm in Z for the ground 
based stereovision data (Burkhard et al., 2012) and 
e  0.5-1.0 pixels, i.c. 3-5 cm, in X and Y and 0.1-0.2 9/4, hg or 
1-2 pixels, i.e. 4-10 cm, in Z for the airborne imagery 
The analysis of the coordinate differences for the 40 points 
yielded standard deviations of the differences of approx. 5 cm in 
X and Y and better than 10cm in the vertical direction. 
Assuming similar planimetric accuracies for both systems this 
leads to a point coordinate accuracy of 3.5 cm in X and Y (for 
each system). The standard deviation of the vertical differences 
is also consistent with the a priori values for the Z component. 
These first investigations also revealed some systematic 
differences between ground-based and airborne coordinate 
determination in the order of 10 cm in planimetry and 10 cm in 
height for each driving direction, i.e. for each ground-based 
trajectory. This is consistent with the expected direct georefe- 
rencing accuracy of the ground-based system in the challenging 
urban environment of the tests. In subsequent experiments, the 
georeferencing approach described in 5.1 will be modified and 
very likely improved, by co-registering the ground-based 
imagery to the airborne imagery using the integrated geo- 
referencing approach described in Eugster et al. (2012). 
  
  
6.4 Accuracy of extracted 3d point clouds 
Dense 3d point clouds were extracted for both the ground-based 
and airborne imagery using the dense matching algorithms and 
tools discussed in sections 3.2 and 4.3.2. Figure 7 shows the left 
part of a raw depth map extracted from the corresponding stereo 
pair and overlaid with the left stereo partner. A postprocessed 
version of this depth map is also used for 3d monoplotting (see 
Figure 6). Textured 3d point clouds can easily be derived from 
these depth maps by projecting the image and depth information 
into object space. 
  
Figure 7. Left stereo normal image with overlaid dense depth 
map (shown in left half of the image) 
An initial accuracy evaluation of the extracted 3d point clouds 
was performed by using four planar patches on the road surface 
as reference surfaces. These test areas were extracted from the 
following 3d point clouds: 
e 3d point cloud derived by projecting the depth map of a 
single stereo frame into object space (ground-based raw) 
e 3d point cloud derived by fusing the depth maps of multiple 
stereo frames and by projecting the interpolated depth map 
into object space (ground-based interpolated) 
* 3d point cloud derived from an airborne stereo image pair 
(airborne) 
Table 1 shows the typical point densities of 3d point clouds 
extracted from the ground-based and the airborne imagery. The 
table also shows the respective standard deviations and max. 
differences from a plane fitted through the point clouds cover- 
ing the four test patches with an area of ~ 22 m? per patch. The 
preliminary results of the ground-based and airborne 3d point 
cloud extractions yield good SDs in the order of 1 pixel or less, 
i.e. <1 cm in the ground-based and « 5 cm in the airborne case. 
  
  
  
  
  
  
  
  
  
ground- | ground- | airborne 
based based 
raw interpol. 
avg. point density [pts / m^] | 1297 3326 109 
avg. standard deviation [m] | 0.009 m | 0.008 m | 0.045 m 
max error [m] 0.052 m | 0.045 m | 0.167 m 
  
80 
Table 1. Typical point densities of different point cloud data 
sets together with their accuracies (standard deviations and max 
difference from a plane fitted into the respective point cloud) 
7. CONCLUSIONS AND OUTLOOK 
The combination of high-resolution airborne and ground-based 
imagery and their integration into predominantly image-based 
3d modelling and 3d geoinformation services provides a 
powerful solution for future road infrastructure management.