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Figure 5. 3D approximate model 
3.4 3D object model reconstruction 
This is the final part of the algorithmic framework and normally 
the main subject of current research. The 3D object model, as it 
is presented in Figure 6, is created when image matching 
(Atkinson, 1996; Zheltov and Sibiryakov, 1997; Grün, 1998) 
occurs between corner points of the 2D model in the image 
space that initially extracted and image areas that belong to the 
neighborhood of projected points in the other images. 
  
Figure 6. The 3D object model 
4. DISCUSSION - CONCLUSIONS 
The 3D object reconstruction is one of the most fundamental 
procedures in Photogrammetry. The relevant research for full 
automation of the reconstruction process still remains under 
question. In close range photogrammetric applications the main 
point is that objects in images are characterized by abrupt 
changes in surface in contrast to aerial case problems. These 
discontinuities must be taken into account in object modeling 
and surface reconstruction. In this paper a developed 
algorithmic framework for 3D object reconstruction under close 
range conditions is described. The whole process consists of 4 
steps in turn and they involve issues like image processing, 
Hough Transform and image matching. 
By default, 3D object reconstruction indicates the existence of 
breaklines. Through the breakline detection, a correct DTM 
calculation is feasible. The existence of breaklines denotes 
approximation for the surface model and this is a vital constrain 
for accurate DTM generation. Moreover, orthoimage production 
is feasible as well. A by-product of the described process is the 
use of the reconstructed 3D wireframe model as a source for 
computing image exterior orientation, in the case where no 
control points are available. 
In any case, research for 3D object reconstruction under close 
range conditions will keep on until user intervention reduced to 
the minimum. 
REFERENCES 
References from Journals: 
Canny, J., 1986. A computational approach to edge detection. 
IEEE Transactions on Pattern Analysis and Machine 
Intelligence, 8(6), pp. 679-698. 
Ballard, D. H., 1981. Generalizing the Hough Transform to 
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122. 
Duda, R. D. and P. E. Hart, 1972. Use of the Hough Transform 
to detect lines and curves in pictures. Communication of the 
ACM, 15(1), pp. 11-15. 
Illingworth, J. and J. V. Kittler, 1998. A survey of the Hough 
Transform. Computer Vision, Graphics and Image Processing, 
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Streilein, A., 1994. Towards Automation in Architectural 
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References from Books: 
Atkinson, K. B., 1996. Close range Photogrammetry and 
Machine Vision. Whittles Publishing, United Kingdom. 
Ballard, D. H. and C. M. Brown, 1982. Computer Vision, 
Prentice-Hall Inc., Englewood Cliffs, New Jersey, pp. 123-131. 
Gonzalez, R. C. and R. E. Woods, 1992. Digital Image 
Processing. Addison-Welsey, MA. 
Mikhail, E. M., J. S. Bethel, J. C. McGlone, 2001. Introduction 
to modern Photogrammetry. Wiley, New York. 
Pratt, W., 1991. Digital Image Processing. (2nd ed.), Wiley, 
New York. 
References from Other Literature: 
Adamos, C. and W. Faig, 1992. Hough Transform in Digital 
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