XIX-B3, 2012
d planes to serve
t clouds derived
rary, and one ad-
, taken from the
On the one side,
ly shaped build-
ult objects as L-
rated facades or
, and stairs. We
our algorithm in
lves during all
1000 iterations.
iaset to another,
a known metric
ute value of this
major four verti-
rts, such as bal-
g models, so far.
onding to minor
ien constructing
with this refine-
t level-of-detail.
21s by additional
OD 2 models.
our major walls
loud consists of
sfully detected,
£g cuboid-based
1 from multiple
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 & Left: Image of building consisting of four major walls
standing on a slope. Right: The derived point cloud consists of
273 000 points, the four major walls were successfully detected,
segmented and connected.
Figure 9: Left: Image of building consisting of four major walls
having a dominant entrance and stairs at one side. Right: The
derived point cloud consists of 271 000 points, the four major
walls were successfully detected, segmented and connected.
images. It detects vertical planar surfaces assuming the dom-
inance of orthogonal intersections. The approach was tested
with point clouds of buildings from image matching with vary-
ing scale, point density and amount of noise and has proven its
robustness.
In future, we want to produce a closed building footprint harmo-
nizing the height of each wall and its neighboring walls. Then we
are able to estimate the top and the bottom borders of the build-
ing, yielding a final closed polyhedral for the building. i.e., its
LOD | model. Afterwards, we will refine the model to models
with more details, LOD 2 and higher. Also, we anticipate as a
future work, the integration of many more geometric primitives
such as spheres, cones, and cylinders.
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Figure 10: Left Image of building consisting of four major walls
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Figure 11: Left: Image of building consisting of four major walls
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