The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008
644
method does not work properly in this situation. Figure 3 shows accurately estimates camera pose and building dimensions
the recovered camera pose and wire frame of the Burnside Hall provided that accurate image measurements are available,
using MatLab.
Unit: mm
Dimensions
measured
using a ruler
Dimensions computed
from the image
Left
Box
Width
V
71.1
73.1
M
70.9
Height
V
123.1
97.9
M
122.6
Right
Box
Length
V
72.1
68.8
M
71.7
Width
V
50.6
47.6
M
49.6
Height
V
15.3
6.3
M
14.8
Table 5. Comparison of the vanishing points based method with
the model based method using real image Fig2.a
Figure 3. Visualization of the recovered camera pose and wire
frame of the Burnside Hall
4. CONCLUSIONS
This paper presented a method to recover 3D rectilinear
building models from single monocular images. The method
uses the correspondences between predefined 3D models and
their corresponding 2D images to obtain camera pose as well as
parameters of 3D building models. The camera orientation is
first recovered followed by solving translation and the first
building model dimensions. The direct computation of the
initial estimate for camera rotation effectively solved problems
in the previous approaches (e.g., Taylor and Kriegman, 1995),
and the determination of camera pose as well as the first
building model dimensions are much simpler than the previous
methods (e.g., Debevec et al., 1996). Under the assumption of
flat terrain, more 3D building models can be reconstructed
based on recovered camera pose through model-to-image
correspondence.
Simulation experiments were carried out in order to investigate
how the accuracy of the algorithm would be affected as
different parameters were varied. The comparison using
identical synthetic and real data shows that our method is
significantly superior over the vanishing points based method.
The experiments also show that our method robustly and
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