Due to the flexibility for extracting 3D line features from
different data sources and the advantage of data fusion, the
authors proposed, in this study, a scheme comprising
Construct-Shape-Refine (CSR) three steps and developed a
line feature-based geometric inference algorithm by fusing 3D
line features with 2D image lines for building roof
reconstruction. Figure 1 illustrates the flowchart of the proposed
CSR method for the generation of 3D building roof models.
2. 2D AND 3D LINE FEATURE EXTRACTION ENGINE
At current stage where the accuracy of line features is not the
main concern, we consider simple but reliable extraction tools
for acquiring 3D line features from imagery and laser range
data, respectively. The algorithm starts by extracting 3D line
features from laser range data by using the semi-automatic 3D
line feature extraction engine, which contains three major
procedures: (1) interpolating the dispersed laser point clouds
into regular grid data, then forming the range images; (2)
automatically extracting 3D line features by using Canny
operator with Hough transform; (3) considering the reliability
for extracting these 3D line features, a user interface which
fuses the human intervention was implemented to select the best
3D line features obtained in last step and to make up for the
deficient parts that resulted from automatic extraction, if any.
The effectiveness of the above design can be illustrated through
the demonstrations of Figures 2 and 3, where the blues lines
present the 3D line features extracted by image processing and
the red one is the feature selected by a user. For those line
features of building roof that miss in the detection, the operator
would undertake manual measurements, as the green line shown
in Figure 3. By way of the semi-automatic extraction, complete
3D line features of building roofs out of LIDAR point clouds
can be obtained, as those black lines in Figure 4 illustrate.
As for 2D line feature extraction from aerial imagery, the (2)
and (3) steps for 3D line features extraction are utilized.
CSR METHOLOGY
After 3D line features are extracted and regarded as input data
for the CSR algorithm, then the procedures of building roof
reconstruction are performed through the following geometric
inferences: (1) constructing the topological relationship of 3D
line features that belong to the same building roof by using the
special intersecting property of 3D line features when projected
onto a plane; (2) shaping the initial building roof by means of
adjusting the 3D line features, and compensating missing parts
by the shortest path algorithm, if any, and reporting whether or
not the investigated building roof is completed; (3) refining the
building roof automatically or semi-automatically by
integrating 2D line features observed from the images through
2D and 3D geometric inference processes. The function of the
automatic mode is not only to find the building boundaries from
images by using the distance, angle and topological checks in
images and object space, but also adjust the 3D line features
obtained either from laser range data or aerial images; on the
other hand, in the semi-automatic refined mode, there are three
modules that are separated by the feature types of
measurements, namely point-based, line-based and hybrid mode.
The point-based model is the traditional photogrammetric
approach for building reconstruction where the first step is to
measure the conjugate points in the comer point order, either
clockwise or counter-clockwise, then execute the point-based
intersection followed by connecting the roof comers through a
CAD design. The line-based mode is nevertheless based on line