1 Introduction
The needs for 3D
city models are rapidly growing in various fields such as virtual city reality,
town planning, microclimate investigation, transmitter placement in
telecommunication, monitoring and protection of coastal zones, pipelines and
corridor mapping and exhaust spreading in urban areas, Et cetera . There is a
steady shift from 2D-GIS toward 3D-GIS now, therefore a great amount of
accurate 3D city models have become essential to be produced in a short period
of time and provided on the market [TAKASE et al]. Now a day, Light Detection
and Ranging (LiDAR) is widely applied in urban 3D data analysis.
2 LiDAR Overview
Mounted on the
aircraft, collecting the LiDAR data, is a Global Positioning System (GPS), a LiDAR
sensor system and an Inertial Navigation System (INS). The GPS returns the longitude
and latitude coordinates of the aircraft’s actual position. The LiDAR sensor
system is a remote sensing instrument which consists of an emitter and a
receiver. The INS tracks the altitude of the LiDAR sensor. The emitter sends
out electromagnetic radiation (a pulse of light) into the atmosphere down to
the target. The receiver (telescope) measures the intensity of the signal
scattered back to the sensor. The time from sending to return of pulse is also recorded
thus the distance of the sensor to the target can be calculated. In the case of
a building surface, the laser will reflect off of the building’s surface and
return to the sensor. Intensity and path of the electromagnetic radiation
(laser beam) are distorted by the interaction
between the beam and the atmosphere itself [NICHOLAS]. An example of LiDAR
sensor system is shown in figure 1.
When two
different heights of one point are received, this gives an indication of the
presence of a penetrable object (tree). In contrast, for a non-penetrable
object (building), data point has the same height for first and last returns.
Figure 2 shows first and last return in case of a tree [ABDULLATIF].
2.1 Ground
Surface
The point
density is high around the scanner and gradually decreases with distance away from
the scanner, the region of interest can be selected for example as an area 20 m
x 20 m around the instrument to reduce the amount of no data areas [CHRISTOPH].
Figure 3 shows the representation of a manmade object (building) and a natural
feature (tree) [VOLKER].
2.2 Tin Procedures
Triangulated
Irregular Networks (TINs) are used for representing or interpolating point clouds
by surfaces. TINs represents a meshing of points (xk, yk, zk)
in the form of triangular surface in 3D with these points as vertices,
constructed above a 2D triangulation of the surface (xk, yk) of
the vertices in a base plane. Triangulations are usually constructed using the
‘Delaunay’ principle, which tells that circumcircles of the triangles do not
contain the locations of vertices in their interior [CHRISTOPH]. Figure
4 shows the mesh points and TIN surface.
2.3 Greedy
Insertion Triangulation
For all
triangles, the distances between the triangles (planes) as shown in figure 5
and the points that they encompass (in x and y or longitude and latitude
spacing) are calculated [NICHOLAS].
3 3D
Reconstruction Algorithm
The following
attributes should be a part of an ideal 3D reconstruction algorithm. The algorithm
should be able to intelligently recognize complex (non planar) building
structures besides simple ones. The algorithm should be generalized such that
it has capability of analyzing raw (irregularly spaced) LiDAR data as well as
where the LiDAR data is rasterized / grid-ed. The algorithm should reconstruct
the buildings in the form of simple structures (roof planes, walls, etc.) as
opposed to a collection of LiDAR points [NICHOLAS]. Figure 6 shows system block
diagram.
3.1 Building
Detection
The first step
of building reconstruction algorithm is the detection of buildings. There have been
many attempts to detect buildings using LiDAR data. The LiDAR points are
classified according to terrain, buildings or others like vegetation.
Morphological opening filters are have been used to determine a digital terrain
model (DTM) which is subtracted from the digital surface model (DSM). An
initial building mask is obtained by applying height thresholds to the
normalized DSM [ROTTENSTEINER et al]. Figure 7 shows an example of a DSM
[NORBERT et al].
3.2 Building
Extraction
Figure 8 shows
work flow for building extraction from LiDAR data [ROTTENSTEINER et al].
The algorithms
for extraction of the building geometrical parameters will be performed after the
complete edges of buildings have been detected. The LiDAR data interpretation incorporates
following facts: (1) The buildings are higher than the surrounding topographic surface;
(2) The laser penetrates into vegetation, thus giving echo from various
heights, makes it possible to distinguish between man-made objects and
vegetation [GUOQING et al].
3.3 Building
Reconstruction
Object
reconstruction or recognition presumes knowledge about the perceived objects by
some kind of object model. Model that is used for building reconstruction
should be able to describe buildings of various complexities and it should
allow the representation of geometric constraints during the reconstruction.
Object models can be visualized as abstractions of real world objects. The most
important part in a model definition is the proper balance between the
tractability and correctness, i.e. the outcome of the model must be adequate
both in terms of the solution attained and the cost effectiveness [NORBERT et
al]. Figure 9 shows an example of a refined reconstruction.
4 Uncertainty
Representing
uncertainty can be a difficult problem. The uncertainty of all geometric entities
like points, lines, planes and objects, can be represented derived by
construction from those entities. But how certain is a segmentation result, for
example, when multiple cues from different sources need to be combined? How
that case is handled when a small (continuous) difference in geometry leads to
a different roof topology (a discrete difference)? [CLAUS].
5 Conclusion
Recent development in
3D displays, computer graphics hardware and real-time texturing as well as the
increasing availability of animation software tools have resulted in an
increased demand for realistic three- dimensional virtual reality city models.
This demand can only be satisfied by capturing urban scenes efficiently, which
presumes the integrated use of various data sources [NORBERT et al]. In the
near future, research will have to find a way to develop new specialized
algorithms for the case, and devise methods to combine aerial and terrestrial sources
efficiently [CLAUS]. Figure 10 shows an example of a virtual city model.
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