o XXXIX-B3, 2012 
5. A normal vector is 
Gridding is normalized 
ormal vectors in the grid 
jMuced in the X, Y, Z 
nels. The normal vector 
hat normal vector maps 
the slope of the roof by 
lirections indicate each 
ns show North and East 
Z direction shows object 
  
or map 
  
1ormal vector map 
  
iormal vector map 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
3.2 Rough shape extraction 
The 3D rough object shape is extracted from the normal vector 
map via image processing. The image processing procedures are 
as follows. 
- Binarizing using Otsu's threshold method (Otsu, 1980) 
- Noise reduction for small objects such as telegraph poles and 
Cars 
- Thinning and line tracing 
Figure 6 shows the resulting 3D rough shape using image 
processing. 
ou er > 
a 
3 n d B 
Figure 6. 3D rough shape 
3.3 Image shape extraction 
The rough object shapes are converted into multiple image 
coordinates by the collinearity conditions. Images around the 
object are clipped in order to limit edge extraction processing 
(Figure 7, Figure 8 (a, b)). The flight direction is from west to 
east and clipped images are rotated 90 degrees, as shown in 
Figure 7, 8. The Canny operator is used to extract object edges 
from clipped digital images. The Canny operator result is shown 
in Figure 9 (a). Please note that the Canny operator result is not 
binarized at this stage in order to use the threshold, depending 
on the situation. The edge potential map (Figure 9(b)) was 
created according to the distance from the point of the edge that 
is considered to exist when a point around the laser has been 
converted. Edge candidates (Figure 9(c)) that were computed 
using the edge potential map and the Canny operator result 
using Otsu's threshold method were thinned. The thinned edge 
candidates are reliable; however, more than one object is 
disconnected. All edges are extracted by minimum threshold 
value in order to connect the edges (Figure 9(d)). This will be 
the connected endpoint of two edge candidates that are included 
in the complete edge. 
An object is extracted from triplet images that are taken as 
multiple images in this paper. Occlusion changes the shapes of 
objects in images with different perspective centers. Therefore, 
the intersection image was calculated using triplet images in 
order to reduce the edge mismatch caused by occlusion. The 
intersection image is created by affine transformed images that 
are transformed using converted rough image shape coordinates. 
The intersection image is shown in Figure 10. A blunder mask 
is computed for each image using the blue part, which indicates 
the occlusion location and changes in the intersection image. 
Finally, a 3D model is created using the extracted edges and 
feature points, which are calculated using the collinearity 
conditions. 
  
     
   
  
(a) left image (b) right image 
Figure 8. Clipped digital camera images 
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Dé 
D 
(a) Canny operator result | (b) Edge potential map 
  
  
    
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+ 
4 M d 
d te 
» 4 Z s > y ; V i 
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(c) Edge candidates (d) All edges by Canny operator 
Figure 9. Edge extraction process images