A Hybrid Fuzzy Heuristic for Point Data Reduction in Reverse Engineering

As modeling and visualization applications proliferate, there arises a need to reduce three dimensional unorganized data points in reverse engineering. To meet the demand for both geometric and engineering fidelity of the reduction, a fuzzy-clustering-based reduction method is presented. As an effective extension to the existing pure geometric reduction methods, a hybrid heuristic is introduced. It includes descriptions of samples’ fuzzy imperative attributes and fuzzy geometric attributes. Reduced points favor to gather at regions of high curvature and surface boundaries. Detailed features, which are particularly valuable for machining, can be well preserved. The method works directly on the point cloud, requiring no intermediate tessellation. The algorithm is experimented on different models and show reasonable results.