ISPRS Commission III, Vol.34, Part 3A ,Photogrammetric Computer Vision“, Graz, 2002 
  
block model obtained from converting 2 1/2 dimensional 
GIS (geographic information system) data. An overview 
of a part of our 3D block model can be seen in Figure 1. 
The 3D block model is augmented using image sequences 
captured by a digital consumer camera from arbitrary po- 
sitions. Currently, we use a Canon D30 with a geometric 
resolution of 2160 x 1440 and 12bit per pixel radiomet- 
ric resolution. The images have to be captured using short 
baselines, thus a digital video camera with a reasonably 
high resolution will work as well. Our work flow consists 
of five consecutive steps which will be explained in the fol- 
lowing subsections. 
3.1 Line Extraction and Vanishing Point Detection 
The line extraction stage starts with an edge detection and 
edge linking process and yields contour chains with sub 
pixel accuracy. For all contour chains of sufficient size a 
RANSAC [3] based line detection method is applied. Pairs 
of contour points are randomly picked from the contour 
and a potential line segment is formed. For this line seg- 
ment inlier points, that are points with a small perpendicu- 
lar distance to the segment are searched. The line segment 
with most inlier points is considered the best hypothesis for 
the line. The final line parameters are estimated by a least 
squares adjustment over the inlier points. A final grouping 
process merges collinear segments that lie close to each 
other. 
The vanishing point detection is based on the method pro- 
posed by Rother [9]. In this approach the previously ex- 
tracted line segments are used for the detection. Each in- 
tersection is treated as potential vanishing point and the 
weight for the intersection is determined by testing against 
all other line segments. The smaller the angle difference 
between a line segment and the vector pointing from the 
mid point of the segment to the intersection, the higher 
the contribution to the weight of the accumulator cell for 
the potential vanishing point. If the angle difference is too 
large, the weight of the accumulator cell is not increased. 
The intersection with the maximal weight is then reported 
as vanishing point. 
3.2 Relative Orientation of Image Pairs 
We developed two completely different methods to cal- 
culate the relative orientation of image pairs. The first 
method is based on calculating corresponding points within 
an image pair. Therefore an area based hierarchical matcher 
is used. In the second approach vanishing points are used 
to solve the relative orientation problem without the neces- 
sity to perform time consuming point to point correlation 
in image pairs. 
3.2. Relative Orientation from corresponding points 
In order to determine the relative orientation of an image 
sequence, we need to find corresponding points in all adja- 
cent image pairs. In our approach we focus on an iterative 
and hierarchical method based on homographies to find 
this corresponding points inspired by a work published by 
Redert et al. [8]. For each input picture an image pyramid 
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is created and the calculation starts at the coarsest level. 
Corresponding points are searched and upsampled to the 
next finer level where the calculation proceeds. This pro- 
cedure continues until the full resolution level is reached. 
This hierarchical method convergences fast and avoids lo- 
cal minima solutions especially when having repetitive struc- 
tures within a facade. 
  
(a) First input image 
  
(b) Second input image 
  
(c) Visualization of our accuracy measure; Crosses indi- 
cate corresponding points used for the calculation of the 
relative orientation. 
Figure 2: Input images and visualization of our accuracy 
measure. 
A reliable calculation of the relative orientation can be done 
using a set of corresponding points which should fulfill 
some properties. They have to be well distributed over the 
images with a good location accuracy and a low outlier 
rate. To achieve this requirement it is necessary to calcu- 
late an accuracy measure for all calculated corresponding 
points. This is done during the matching process and is 
calculated within a cost function based on the distribution