Full text: Proceedings of the CIPA WG 6 International Workshop on Scanning for Cultural Heritage Recording

- 83 - 
can not be estimated. However, we can measure the bounding 
cuboid and compare it with the dimensions of the model. Table 
1 summarizes the results. The resulting models, shown from 
three views, are depicted in Figure 16. All models are built with 
an octree resolution of 256 3 and using 360 views. 
Vessel #1 
1 
> 
% 
• 
» 
m 
4 
V 
V 
Sherd #1 
Sherd ii2 
Figure 16. 3D models of two vessels, two sherds and a cup 
6. CONCLUSION 
Many In this paper a combination of a Shape from Silhouette 
method with a shape from Structured Light method was 
presented, which create a 3D model of on object from images of 
the object taken from different viewpoints. It showed to be a 
simple and fast algorithm, which is able to reconstruct models 
of arbitrarily shaped objects, as long as they do not have hidden 
concavities, i.e., concavities not visible in any of the input 
images. For concavities visible to the active system, results 
show that the computed volumes provide the correct model. The 
algorithm is simple, because it employs only simple matrix 
operations for all the transformations and it is fast, because even 
for highly detailed objects, a high resolution octree (256 3 
voxels) and a high number of input views (36), the 
computational time hardly exceeded 1 minute on a Pentium II. 
Already for a smaller number of views (12) the constructed 
models were very similar to the ones constructed from 36 views 
and they took less than 25 seconds of computational time. 
For archaeological applications, the object surface has to be 
smoothed in order to be applicable to ceramic documentation, 
for classification, however, the accuracy of the method 
presented is sufficient since the projection of the decoration can 
be calculated and the volume estimation is much more precise 
than the estimated volume performed by archaeologists. 
7. REFERENCES 
[1] H. Baker. Three-dimensional modelling. In Proc. of 5' h Inti. 
Conf. on AI, pages 649-655, 1977. 
[2] P. Besl. Active, optical range imaging sensors. MVA, 
1(2): 127-152,1988. 
[3] H. H. Chen and T. S. Huang. A survey of construction and 
manipulation of octrees. Computer Vision, Graphics, and 
Image Processing, 43:409-431, 1988. 
[4] C. H. Chien and J. K. Aggarwal. Volume/surface octrees 
for the representation of three-dimensional objects. CVGIP, 
36:100-113,1983. 
[5] R. M. Haralick and L. G. Shapiro. Glossary of computer 
vision terms. PR, 24(l):69-93, 1991. 
[6] M. Kampel and R. Sablatnig. On 3d Modelling of 
Archaeological Sherds. In Proceedings of International 
Workshop on Synthetic-Natural Hybrid Coding and Three 
Dimensional Imaging, Santorini, Greece, pages 95-98, 
1999. 
[7] M. Kampel and S. Tosovic. Turntable calibration for 
automatic 3D-reconstruction. In R. Sablatnig, editor, Proc. 
of 24th AAPR Workshop, pages 25- 31,2000. 
[8] K. Kutulakos and S. Seitz. A theory of shape by space 
carving. IJICV, 38(3): 197-216, 2000. 
[9] C. Liska and R. Sablatnig. Estimating the next sensor 
position based on surface characteristics. In ICPROO, 
volume I, pages 538-541, 2000. 
[10] W. N. Martin and J. K. Aggarwal. Volumetric description 
of objects from multiple views. PAMI, 5(2): 150-158, 1983. 
[11] C. Menard and R. Sablatnig. Computer based Acquisition 
of Archaeological Finds: The First Step towards Automatic 
Classification. In H. Kamermans and K. Fennema, editors, 
Interfacing the Past, Computer Applications and 
Quantitative Methods in Archaeology, number 28, pages 
413-424, Leiden, March 1996. Analecta Praehistorica 
Leidensia. 
[12] V S. Nalwa. A Guided Tour Of Computer Vision. 
AddisonWesley, 1993. 
[13] W. Niem. Error analysis for silhouette-based 3D shape 
estimation from multiple views. In N. Sarris and M. 
Strintzis, editors, Proc. of Inti. Workshop on Synthetic- 
Natural Hybrid Coding and 3D Imaging, pages 143-146, 
1997. 
[14] J. O'Rourke and N. Badler. Decomposition of 
threedimensional objects into spheres. IEEE Transactions 
on Pattern Analysis and Machine Intelligence, PAMI-1 
(3):295-305, 1979. 
[15] C. Orton, P. Tyers, and A. Vince. Pottery in Archaeology. 
1993. 
[16] M. Potmesil. Generating octree models of 3D objects from 
their silhouettes in a sequence of images. CVGIP, 40:1-29, 
1987. 
[17] Y. Shirai. Three-Dimensional Computer Vision. Springer- 
Verlag, 1987. 
[18] S. K. Srivastava and N. Ahuja. Octree generation from 
object silhouettes in perspective views. CVGIP, 49:68-84, 
1990. 
[19] R. Szeliski. Rapid octree construction from image 
sequences. CVGIP: Image Understanding, 58(1 ):23- 
32,1993. 
[20] S. Tosovic. Lineare Hough-Transformation und 
Drehtellerkalibrierung. Technical Report PRIP-TR-59, 
Institute of Computer Aided Automation, Pattern 
Recognition and Image Processing Group, Vienna 
University of Technology, Austria, 1999. 
[21 ]S. Tosovic and R. Sablatnig. 3d modeling of archaeological 
vessels using shape from silhouette. In Proc. of Conf. on 3- 
D Digital Imaging and Modeling, pages 51-58, 2001. 
[22] J. Veenstra and N. Ahuja. Efficient octree generation from 
silhouettes. In CVPR, pages 537-542, 1986. 
8. ACKNOWLEDGEMENTS 
This work was partly supported by the Austrian Science 
Foundation (FWF) under grant PI 3385-INF, the European 
Union under grant 1ST-1999-20273 and the Federal Ministry of 
Education, Science and Culture.
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.