基于粒子群优化算法的三维点云NURBS曲面重构研究

Research of NURBS surface reconstruction from 3D point cloud based on particle swarm optimization algorithm

针对Delaunay三角化等传统方法在逆向工程曲面重构过程中存在着精准度不足、处理高维非线性问题时效率低等问题,提出一种基于粒子群优化算法的三维点云NURBS曲面重构方法。通过曲面参数化和曲面拟合两个关键步骤,使用粒子群优化算法确定出控制点、节点矢量和权因子等NURBS曲面参数,实现了NURBS曲面重构。最后使用包含了开放、半封闭、封闭、实际扫描、多分支和自我交叉等5种不同类型具有代表性的曲面模型对算法进行验证。实验结果表明,该方法具有良好的鲁棒性、准确性和灵活性。

For Delaunay triangulation and other traditional methods, there are a lack of precision, low efficiency of dealing with high-dimensional nonlinear and other problems in the process of surface reconstruction in reverse engineering, gives a NURBS surface reconstruction method from 3D point cloud based on particle swarm optimization algorithm. Through two key steps which are surface parameterization and surface fitting, this method uses particle swarm optimization algorithm to determine the control point, knot vector, weight and other parameters of NURBS surface, and realizes the reconstruction of NURBS surface. Finally uses five different types and typical surfaces which are containing open, semi-closed, closed, the actual scanning, multi-branches, self- intersections, to verify this algorithm. Experimental results show that the method has good robustness, accuracy and flexibility.