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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
Figure 4. From top left clockwise, a) original image. The top right marble area is new with chiselled surface resulting in many 
repetitive bright dots, b) the blue channel of the noise-reduced image. Noise is reduced but also some signal is filtered out. The 
texture is very weak, c) Same as b) after Wallis filtering [Wallis, 1976]. d) same as c) with about 22.5 deg convergence angle. Note 
the good effects of Wallis mentioned in the text, and the small repetitive texture, which sometimes differ in c) and d) due to different 
viewing angle and marble properties. 
For Mamiya, we tried three different parameter sets of the 
Wallis filter. Visual inspection of the matching results did not 
show any significant differences between the three versions. 
Figure 4 shows an example of Wallis filtering. The large texture 
improvement is apparent. But difficulties in matching such 
images might arise. Indeed, in homogeneous areas which often 
occur with marble surfaces, especially new marble, noise may 
have been enhanced and appear as texture. This underlines the 
importance of using cameras with good radiometric quality. The 
second problem is that when the camera view changes, different 
texture appears in the image due to the crystal structure of 
marble and the different reflectance and automated matching 
becomes impossible. This problem increases with larger 
convergence angle between the images and occurs in both sun- 
illuminated and shadow areas. The last problem is that the 
texture is often of small size and repetitive, leading to multiple 
solutions and wrong results in matching. Occlusions are an 
additional problem. 
4.3 Results of 3D modeling and surface generation 
For the image-based 3D modeling of the Erechtheion, as the 
monument contains different typical architectural elements (like 
columns, flat walls, etc.), manual measurements were applied 
(Figure 5). On the other hand, for some details (Karyatides, 
ornaments, etc.) and to investigate image matching problems on 
marble surfaces, automated approaches for dense surface 
reconstructions were used. For these parts we employed a (i) 
Depth from Shading (DfS) method (Figure 6) (El-Hakim, 2006) 
and (ii) the ETH matcher able to recover dense and accurate 
results using simultaneously multiple images (Zhang, 2005; 
Remondino et al., 2008). The method combines multiple 
matching primitives and various area-based matching 
techniques to exploit all the content information of the images. 
It can also cope to a large extent with depth discontinuities, 
wide baselines, repeated patterns, occlusions and illumination 
changes by using several advancements over standard stereo 
matching techniques. Figures 7 and 8 show the 3D results from 
the Canon and Mamiya camera respectively. 
To evaluate the accuracy of the image matching results and 
investigate possible penetration effects of laser light into the 
marble (Godin et ah, 2001; Lichti and Harvey, 2002), we 
compared the photogrammetrically derived point clouds with 
the range data (Surphaser 25HSX, range accuracy specification: 
lmm@15m, see www.md3d.uk.com/surphaser.html) acquired 
for the range-based modeling of the entire Erechtheion (El- 
Hakim et ah, 2008). The average point density of Surphaser 
was 2 mm, and for the matching surface models 1 mm and 2mm 
for the Canon and Mamiya respectively. The 3D comparison is 
performed within PolyWorks© IMAlign and IMInspect. Firstly, 
the two meshes have to be co-registered (rotation, translation 
and scale, the latter estimated manually) and then a best fit 
between them is computed, minimizing the distances between 
the mesh patches.