## 4 Discussion  
This part deals with the analysis of the obtained results in terms of point data filtering and point data reduction. Processing time is discussed in particular with reduction, since it is negligible in point data filtering. In terms of filtering, the first and third case studies were more demanding, which imposed the application of a combination of tools for point data filtering. In the first case study, which is characterized by a large number of impulse errors typical for a laser triangulation digitization, filtering process was carried out without the need for manual user's work, primarily due to efficient outliers' filtering by the method of angle. The third case study is characterized by the need for using multiple filtering tools. Although the efficiency of the volumetric filter, segmented line filter and the end point filter showed satisfactory, object's geometry in this case imposed a need for manual filtering of individual points by the user. Unlike in previous cases, in the second case study is--due to object's geometry and point cloud's features--filtering was effectively carried out using only the segmented line filter. Related to point data reduction, it has to be emphasized that the presented approach allows the user to conduct the reduction process qualitatively by defining the desired value of the maximum tolerance, _i.e._, maximum allowed deviation. This is--compared to the quantitative approaches based on the reduction percentage definition or approaches based on setting (usually abstract) parameters' values--more intuitive in terms of the expected quality of the reconstructed model. The developed approach is validated through its implementation in the three different sampling methods for point data reduction, showing--as demonstrated through three case studies--analogous efficiency in all three methods. Common features of the first and the second case studies is the objects' geometry, characterized by significant difference in _z_-coordinate values within cross-sectional point data arrays, which implied an intermediate reduction levels. However, in the first case study for a four times lower level of _MAD_ (0.01 _versus_ 0.04 mm) achieved level of reduction was higher for about 12%. Nevertheless, it should be considered that the overall dimension of the object in the first case study was 25 times smaller, as well as that the object's surface from the second case study was smoother. The object of a third case study is characterized by the simpler geometry that implicated a very high level of reduction of almost 98%. This information should be considered with the facts that this object was approximately three times smaller from the object in the second case study, as well as that reduction was performed with lower level of _MAD_ for 0.1 mm (0.03 _versus_ 0.04 mm). Smaller size of the object implies a finer resolution, _i.e._,  
Figure 25: Cont. higher density of point data, which generally provides a higher reduction level for a given level of MAD. As for the geometric complexity of the scanned object, a smoother object's surface implies the achievement of higher reduction level. The processing time can be taken as a conditional drawback of the presented reduction approach. Because of the high dependence of the reduction process on: object's geometry, level of _MAD_, and (_cross-sections no._)/_(no. of points in cross-section)_ ratio, it is not possible to express the average speed reduction, but it can be roughly estimated at about 350,000 points per hour, for _MAD_ of about 0.05 mm. The processing time in the presented case studies ranged from about 25 to 95 min, noting that the results were achieved on a PC configuration of medium-high performance. With respect to the mathematical background of the developed algorithms, one can expect better results on more powerful PC platforms.