Coarse to Fine Extraction of Free Form Surface Features

Abstract

Freeform surface features (FFSFs) extraction is one of the key issues for redesigning and reediting the surface models exported from commercial software or reconstructed by reverse engineering. In this paper, a coarsetofine method is proposed to robustly extract the FFSFs. First, by iterative Laplacian smoothing, a set of height functions are generated, and principal component analysis (PCA) is employed to obtain the appropriate iteration number for the feature field extraction that is then accomplished by the Gaussian mix model (GMM) with a high segmentation threshold. Second, based on the feature field, an adaptive smooth ratio for each vertex is proposed for Laplacian smoothing, which is implemented to generate a precise base surface. Thereby, with the base surface, the FFSFs can be easily extracted by using the GMM. The empirical results illustrate that the proposed method yields improved performance for extracting FFSFs compared with conventional methods.