An improved boundary extraction method of STL model based on edge curvature estimation
Jingbin Hao1, Liang Fang2, Haifeng Yang1
1College of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China
2State Key Laboratory of Mechanical Behaviour of Metals, Xi’an Jiaotong University, Xi’an, 710049, China
To efficiently extract feature boundaries of the STL model, an improved method is proposed based on edge curvature estimation. Three curvature parameters (dihedral angle, perimeter ration and convexity) are used to estimate the surface curvature information of the STL model. Genetic Algorithm (GA) is used to determinate the threshold of feature edges. The extracted feature edges are grouped and filtered using the best-fit plane (BFP), which is calculated by Least Square Method (LSM). The Dijkstra’s algorithm is used to close the incomplete feature boundaries. Several experimental results demonstrate that the amount of feature edges is significantly reduced, and useful feature edges are reserved to construct feature boundaries. The improved boundary extraction method has important significance in decomposing large complex STL models meaningfully.