## 1 Introduction  
Current demands of a globalized market for constant shortening of development time for novel and increasingly complex products--in order to maintain a competitive edge--have made Reverse Engineering (RE) an extremely popular technique, to the point of being practically indispensable in some design problems. The market today dictates sudden and frequent re-design of products with emphasis on aesthetic and ergonomic features, which, in turn, require the ever more complex organic forms and shapes. These are most often very difficult--sometimes even impossible--to model using conventional CAD tools, which is why they first have to be modelled by sculpting (using clay, plaster, wood, _etc._), and subsequently translated into CAD models using RE [1; 2; 3; 4; 5]. Besides, application of RE can substantially speed up and improve the process of re-design, regardless of whether it be own- or competition products. Also in the case of copying, or reproducing parts and products which are not accompanied by adequate engineering documentation there is practically no alternative to the RE technique [6; 7; 8]. Modern 3D digitization systems, which are employed by RE, feature ever-growing scanning speeds with the ability to generate large quantities of points in a unit of time [9; 10; 11]. Generally speaking, that is advantageous for the quality and efficiency of RE modelling. However, the huge number of point data, generated in the course of 3D digitization, can become a serious practical problem, later on, when the CAD model is generated. In addition, the 3D digitization process is very often plagued by measuring errors, which can be attributed to the very nature of measuring systems, various characteristics of the digitized objects and subjective errors by the operator, which also contribute to problems in the CAD model generation process [12; 13; 14; 15]. Fundamental problems--caused by erroneous point data and a huge number of point data as the result of 3D digitization--are: deviations in shape of the resulting CAD model as compared to the original physical object, and impeded work with software applications for CAD model generation [2; 16; 17]. The process of surface model generation can be significantly slowed down, and in some extreme cases even brought to a complete halt, despite the high processing power of modern computers. Considering all this, one can conclude that the data pre-processing stage, which includes error filtering, smoothing and reduction of point data, is very important and almost unavoidable in every RE-system. In support of this claim, there is a fact that data pre-processing has for a number of years now been at the forefront of research, with the reduction of point data being one of central topics. The result is a number of developed systems for pre-processing of 3D digitization data, which are based on various approaches, and depend on RE methodology applied, that is, the 3D digitizing system for which they were developed [18; 19; 20; 21; 22; 23; 24; 25; 26]. In order to improve the quality and efficiency, artificial intelligence methods--fuzzy logic (FL), artificial neural networks (ANN) and genetic algorithms (GA)--have so far been implemented at various stages of RE, whereby they have been most frequently used for data pre-processing, or precisely, point data reduction. [PERSON] _et al_. [27] proposed an approach to predetermine the topology of a surface from the point cloud on the bases of FL. This method has been applied for the predefinition of the different main regions of a surface, in order to be able to divide the point cloud in different separate clouds corresponding to these regions. The main advantage of this method is that, for a given accuracy, the complexity of the surface is lower than a traditional method, and the computationsare quite the same than those obtained when using traditional surface modelling algorithms.