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Contour-Based 3D Point Data Simplification for Freeform Surface Reconstruction

Three-dimensional clouds of largely unorganized coordinate data are often used to reconstruct freeform surfaces and shapes for a variety of seemingly diverse reverse engineering applications involving computer-aided design, anatomical reconstruction, cartography, digital archaeology, and infrastructural renewal. The point cloud data acquired by non-contact digitizers is very dense and includes numerous scanning errors. As a consequence, the captured data must be filtered and simplified for accurate surface reconstruction. Many existing data simplification techniques are, however, complex and do not directly support the development of spline-based surface models. In this paper a novel contour-based simplification algorithm is introduced for creating B-spline facial surface models directly from scanned data. The algorithm first extracts a series of equally-spaced sectioned contours from an unorganized 3D point cloud by mapping points onto a set of user-defined parallel planes. Each extracted contour is then regenerated as a cubic B-spline curve with a reduced number of control points using a user-defined reduction ratio. A freeform surface is finally created from these contiguous reconstructed contours by a lofting process. Deviation analysis that compares the final reconstructed surface to the original point cloud data is used to demonstrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm generates a fairly accurate spline-based surface model from unstructured points using less than 20% of the actual scanned data. Surface accuracies are enhanced with increased number of initial contours and a greater second stage data reduction ratio.