cyber city,
| building
. Since the
'oof-edges
' topology
ppropriate
integrated
f building
'oof-edges
> that high
r building
rk can be
degree of
ction. The
Hard. and
1998) and
| Grün &
nages and
ion. Their
n manual
' building
tabase, to
| to adjust
atures. À
ling types
ry (CSG)
CSG tree
operator
qualified
cture and
'onnected
ions and
sie. the
dels. The
utilizing
nt of 3-D
;'omplete,
lel-based
as roads,
ion, 3-D
d interior
Although
annot be
(1) The
lusions is
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXX V, Part B3. Istanbul 2004
necessary to assure the completeness of a roof unit. (2) The
structuring may fail if the processed roof unit or object is not in
the pre-defined model database. (3) The boundary point
digitizing sequence must be restricted to being point-wise. (4)
Each roof unit or object needs to be processed independently,
which leaves connection problems between two buildings to the
operator.
2. METHOLODGY
In this paper, we proposed an interactive scheme for 3-D
building modeling using multi-view aerial photos with known
absolute orientation parameters. In order to solve the precise
stereo correspondence problem, we combine the manual rough
measurement via monoscopic viewing, followed by an
automatic feature line extraction and image matching technique
to extract the roof-edge's 3-D line-segments. Finally, in 3D
topology and geometry reconstruction, the
SPLIT-MERGE-SHAPE (SMS) algorithm (Rau & Chen, 2003b)
is adopted to generate the 3-D building model from those 3-D
line-segments. The flowchart of the proposed scheme is
depicted in Fig.1.
Aerial Photos ^ i
Abs. Orientation
Rough Building Corners
Digitization
Feature Line Extraction
& Stereo-Matching
3D Line-Segments
SPLIT-MERGE-SHAPE
Building Modeling
Y
Reconstructed
3D Building Model
Figure 1. Flowchart of the proposed scheme.
The major work of this paper is mostly on the interactive
scheme. The idea is to adopt the reliability of human eye and the
calculating power of a personal computer. Additionally, the
whole scheme can also reduce the hardware requirement that
any graphical card is enough with no necessary of expensive
Stereographic card, monitor, etc. For human eye, it can
effectively distinguish the target from massive information of
aerial photos. The computer provides precise estimation from
huge calculation. In figure 1, two shaded-boxes denote the
human-machine interactive part of this research. One for initial
guess of stereo measurement while the other is for geometrical
building modelling.
2.1 Rough Building Corners Digitization
In addition to the original multi-view aerial photos, the absolute
orientation parameters are necessary in this stage. In the
digitisation of building corners, we utilized the top-down
585
ray-tracing method and bottom-up back-projection technique
for manual stereo measurement via monoscopic viewing, as
shown on figure 2. At beginning, the operator can choose one
building corner on the master photo and digitise it roughly.
Combining absolute orientation parameters Gas Yi 76) with
the digitised image coordinates (xi, y;) in the master photo we
can determine the ray direction. The operator can thus adjust or
provide a ground height (Z,) to get a ground coordinates (X;,
Y). By applying the bottom-up technique, the projected photo
coordinates (x», y?) on the other aerial photos can thus be
calculated and projected on the search photo. So, the operator's
workload is simplified to adjust a target's ground height and
match its corresponding point.
rU 2 C
KEY
Master Photo s
> 7 : /
AN?
yr,
u
M
"Xp Yi,
Figure 2. Stereo measurement via monoscopic viewing.
2.2 Feature Line Extraction & Stereo Matching
In 3D building modelling, the SMS algorithm (Rau, 2002; Rau
& Chen, 2003b) was adopted. In which, the primary ground
object is the 3D roof-edges, which can be extracted from 2D
image feature line and stereo matching. A brief description of
every step is discussed in the following.
At first, an operator may manually digitise a sequence of coarse
building corners to get the initial location of building boundary.
Two consecutive building corners will construct an image
feature line. A buffer zone is created around the feature line and
the following image processing technique is applied within the
buffer zone only. A fast straight-line extraction technique
proposed by Rau and Chen (2003a) is adopted in this study. In
which, an SNN filter is utilized for noise reduction and a
multi-resolution edge detection technique is used to get edge
magnitude. A local maximum tracing method will thin the edge
as single pixel width. A modified Hough transform with
principal axis analysis can accelerate the extraction of
straight-line.
There are numbers of feature line stereo-matching algorithms
that have been published in the field of photogrammetry and
computer vision. However, in this paper we adopt the epipolar
geometry constrain and grey value consistence for stereo
matching. Once a feature line has been extracted on the master
photo, the other feature lines on the search photos is also
considered for automatic detection. There may exist no one or
more than one feature lines in the buffer zone. For a set of
four-views stereo photos, the maximum number of stereo-pairs
combination is six. It increases the possibility and redundancy
of stereo matching in a dense and complex environment that
hidden effect may happen. The normalized cross correlation
(NCC) method is utilized for calculating the similarity index. It
is calculated on both side of a feature line and compared
