XXXIX-B3, 2012 
  
t cloud 
d ery (1) 
id the models proposed 
1-Montaut et al., 2005) 
int cloud and the TLS 
m. The maximum dis- 
the dense point cloud 
1 a sigma of 6 cm. The 
Figure 5). 
d in order to better un- 
he sparse IBM point 
is purpose horizontal 
  
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 
  
Figure 5: Distance comparison between a part of the IBM dense 
point cloud with the TLS point cloud 
and vertical sections of the TLS and the IBM point clouds have 
been made. As it can be seen in Figure 6 it is clear that the main 
geometry problem of the sparse point cloud is on the Z axis of 
our IBM mode where there is a systematic difference between 
the TLS point cloud and the IBM point cloud. This difference 
could be due to the fact that the IBM point cloud is very sparse 
on the ceiling and therefore the point cloud is ill-referenced on 
the Z axis. On the other hand the X and Y axis, as it can be seen 
on Figure 7, don't suffer from significant geometry problems we 
can therefore assume that the results would have been signifi- 
cantly better if the ceiling was sufficiently textured.In Figure 5 
we can also notice that during the dense point cloud generation 
MICMAC was not able to sufficiently compensate the distortions 
of the fish-eye lens and therefore the differences between the TLS 
point cloud and the dense point cloud seem to follow, much less 
extensively, the pattern of distortion of the fish-eye lens which 
could be attributed to the poor quality of the optics used for the 
construction of the low cost fish-eye lens that was used in our 
acquisition. 
M Proto 
  
Figure 6: Horizontal section of IBM generated point cloud and 
TLS point cloud) 
de 
d Photo 
Figure 7: Vertical section of IBM generated point cloud and TLS 
point cloud) 
5 CONCLUSIONS AND FUTURE WORK 
We have presented a comparison of TLS and of a fully automatic 
photogrammetric work-flow IBM. We should note that we have 
not compared the IBM results of our software with results of other 
commercial or open source software such as Bundler-PMVS due 
to the fact that to our knowledge these software don't offer the op- 
tion of calibrating fish-eye lenses on which we have solely been 
based for capturing our complex interior scene. However this 
choice was essential to our purpose since it allowed us to capture 
our scene using the fewer possible images with big overlaps and 
thus to a)accelerate the photogrammetric process and b)establish 
that the algorithm would be able to converge to a satisfactory re- 
sult. The overall result of our approach may not reach the geo- 
metric precision of a laser scanner, since it is heavily constrained 
by the lack of texture that characterizes most of the modern build- 
ings, however it still offers an interesting solution to TLS. In fact 
the use of IBM in our case trades part of the TLS accuracy for 
lower cost since the IBM dataset can be captured with any of the 
shelf dSLR camera equipped with a low cost fish-eye lens and an 
open source software instead of using an expensive laser scanner 
and its proprietary software. Another advantage of the IBM mod- 
elling solution compared to the TLS is the significantly faster ac- 
quisition of the images for the model generation, which could be 
essential for applications that demand quick and low cost mod- 
elling of complex interior spaces with low texture instead of a 
very accurate 3D model. Another advantage of the IBM mod- 
elling is its portability since a dSLR camera -or any other digital 
camera- and a tripod can be easily transported by a single per- 
son whereas TLS tend to be heavy and bulky thus making their 
transportation a difficult and sometimes complicated and expen- 
sive task. Another advantage of the IBM method compared to 
the TLS used in our experimentations is that it directly provides 
a high quality textured by the R,G,B information of the images 
whereas the texture in the TLS model is of inferior quality due to 
the significantly lower resolution of its camera. 
We believe that the results of our method can be improved with 
the use of a better quality fish-eye lens. Another step that could 
be added to our approach in order to achieve better results is the 
use of lenses of longer focal length for capturing images, with or 
without sufficient overlap, of higher resolution for certain areas of 
interest or even for the whole scene. The highly overlapping fish- 
eye images would therefore provide the skeleton of our model 
allowing the connection of the longer focal-length images that 
can be used for the generation of dense point clouds. Finally the