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Digital Image Processing
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A Technique for 3D Modelling of Buildings
3. Results and Discussions
Figure I(a) and (b) show an ISPRS test stereo pair ("Flat" area) supplied by the University of Sttutgart. The v" solution of images used for experiments was 24 cm. The height extraction system was applied to this stereo pair. A S-level image pyramid was generated by reducing the images successively by the factor of 2. pyramidal matching was applied to this pyramid and 3146 points were matched (83.6% of to matchable points).These points were convert Into ground coordinates by applying camera model and a Digital Elevation Model DEM) was generated. This DEM IS shown In figure I(c). this DEM had a RMS height error of I.I m compared to a ground-truth DEM. Height information was retrieved successfully using pyramidal matching. However, there are many "holes" in the DEM, in particular on the roofs of buildings. This indicates that the ..height discontinuities due to buildings still remain as an obstacle for height extraction. This is the currentlimitation of the height extraction system.
Figure 1. (a) and (b) : An ISPRS test stereo pair. (c) A DEM from the hight extraction system. (d) A building detection output (c) A perspective view of buildings.
Building detection was performed to the image in figure I(a). After line extraction. 844 lines were obtained From these lines, 996 line relations were found. A line-relation graph was constructed using the 844 lines as
nodes and the 996 line relations as arcs. A depth-first graph traversal algorithm was applied to find closed is loops and "U"-shaped loops in the graph. 60 "normal" building hypotheses (closed loops) and 240 "super" building h)1><>theses ("U"-shaped loops) were found initially. After building hypothesis verification process, 28 "normal" and 22 "super" building hypotheses were finally obtained. Note that there has been a great reduction i r in the number of "super" building hypotheses after the verification process. This is because, in our implementation, every "normal" building hypothesis makes one or more "super" building hypotheses and these rc redundant "super" building hypotheses are removed after the verification process. Figure I(d) shows the 28 "normal" and the 22 "super" building hypotheses. There are 19 buildings in the scene. 10 buildings are fully and 9 buildings are partly detected.
The 3D modelling of buildings was applied by fusing the pyramidal matching output and the buildingdetection output. From the 50 ("normal" and "super") building hypotheses, 23 building hypotheses were , modelled as planar surfaces and 1 building hypothesis an apex surface. However, 26 building hypotheses were c not m.00elled due to insuffic~en~ modelling points or a large estimation error. Al through all Welding roofs are . apex In the scene, many bwldIng hypotheses were modelled as planar surface. this IS vahd because many .if building hypotheses cover only half of building roofs which are planar. Height interpolation was carried out. using these surface models. Fire l(e) shows a perspective view of buildings after the height interpolation. A ~"c constant value of loom was assIgned to the 26 un-modelled building hypotheses to distinguish them from the ground plane. The perspective view shows that 3D modelling was performed successively. Many buildings have apex-shaped roofs. Some low buildings are due to the un-modelled building hypotheses with the constant height of l00 m.
Figure 2(a) and (b) show another test stereo pair supplied by Ern Zurich. The resolution of the images was 15 cm. A 5-level image pyramid was created and the height extraction system was applied to this pyramid as before. 18448 matched points (91% of the total matchable points) were matched. After converting them in ..c ground coordinates, a DEM was generated (figure 2(c". In the DEM, height information even on the roofs of ~ buildings was successfully.
Figure 2.(a)and (b) : Another test stereo pair. (c) A DEM. (d) A building detection output. (e) A perspective view of buildings.
The building detection system was applied to the image in figure 2(b). After line extraction, 338 lines were ,.obtained. 508 relations between these lines were found and a line-relation graph was contracted accordingly.From this graph, 50 closed loops ("normal" building hypotheses) and 210 "U"-shaped loops ("super" building ..hypotheses) were initially generated. After the verification process, 27 "normal" and 8 "super" building ~ hypotheses was finally verified. These are shown in figure 2(d). There are 12 buildings in the scene. Among : them, 8 buildings were detected fully and 3 partly. However, one building in the middle of the scene was , completely undetected. There are three building hypotheses which are not from real buildings but from other ,.. objects (two of these do "look" like real buildings).
The 3D modelling was performed as before. The 35 building hypotheses and the height information in the DE< were combined for height interpolation. 24 building hypotheses were modelled as planar surfaces and 5building hypotheses apex surfaces. 6 building hypotheses were not modelled. Height interpolation was carried out using these surface models. For the 6 un-modelled building hypotheses, a constant height value of 470mpc was assigned. Using these results, a perspective view of buildings was created (see figure 2(e". As shown in: the figure, 3D modelling was performed successfully. Many building roofs have apex-shapes. The flat buildingin the middle of the scene is due to the one undetected building.
4. Conclusions and future work
In this paper, a new technique developed for automated 3D modelling of buildings was briefly described. The4 results shown support the good performance of the technique. This section will discuss some other aspects of the technique.
There are many pyramidal matching algorithms developed and proposed so far. The major difference between the one described here and others is that the problem of blunder propagation was carefully considered. A naivepyramidal matching algorithm without considering this problem may fail to work in a extreme circumstances where, for example, there are a lot of height discontinuities in a scene.The building detection system described here also uses the concept of perceptual grouping but emphasises the connection between lines. The use of type and value of connections between lines is a very unique approach. ---One of the main differences between this building detection system and others is that this system works reasonably well without any verification process (results without verification process, however, were not presented in this paper due to the limited space). Compared to other systems, the number of building potheses removed after the verification process in small. The reason is because building hypotheses aregenerated very carefully in this system. (Other systems may apply "strong" verification process for successful building detection. )
3D modelling of buildings was achieved by combining the height extraction system and building detectionsystem. In is worth noting that this 3.D modelling was done without any man intervention. This approachcan be a good example of the potential benefits due to the fusion of monoscopIc and stereoscopic processes. The major contribution of the work described in this paper is the development of techniques which can be used ~ for automated urban mapping.
There are several aspects to be considered for the future work on this technique. As the 3D modelling is achieved only after quite a number of processes, there are many parameters to be specified by operators. Although most of them can be substitute automatically, some should be carefully chosen. This can be an obstacle for a "truly" automated system. In the building detection system, the verification process may need further development. Compared to other systems, the verification process used here is one of the simplest. This is partly due to the reliability of candidate building hypotheses as mentioned earlier. However, for a more robust system, further development on the verification processes should bring some benefits.
References
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