由于用无序离散点集来重建出曲线曲面模型，在反求工程与计算机视觉中都有着广泛的应用，为此根据实际采样中离散点分布相对集中的特点，提出了一个基于自适应遗传算法的多维无序点集曲线重建算法。该算法针对无序带噪声的空间曲线重建问题，先把点云分布空间网格化，然后在每个网格中用自适应遗传算法搜索出最能代表该网格中点集的特征点，由于每个网格区域中点集分布的不均匀性，因此可根据搜索出来的特征点，利用改进的自适应的SIG（sphere-of-influence graph）图来对每个特征点进行进一步调整，以便能使得到待重建曲线的型值点，最后利用测地距离函数来确定型值点的拓扑结构，并利用B样条函数来重建曲线。实例证明，无论是2维平面点云还是3维空间点云，该点云重建方法简单可行，特别是对于存在自交情况以及点云具有明显角点的情况亦可以获得满意的结果。

Curve reconstruction from a set of unorganized points plays an important role in the fields of reverse engineering and computer vision.In this paper,an adaptive genetic algorithm is presented for curve reconstruction based on the regular distribution property of the point set.In this paper,the algorithm divides the region of point set into many grids and in each grid the algorithm elects a point by the adaptive genetic algorithm.Because of the asymmetry of the date points in each grid,here the sphere-of-influence graph(SIG) with several extensions was investigated,which provides a natural notion of proximity in our context,to adjust each data point which have been elected.This paper make these adjusted data points in order,then can reconstruct the curve using B-spline curve based on these ordered data points.The algorithm presented is feasible and simple,specially not only in two-dimensional situations but also in three-dimensional situations.As to the self-intersect point set and the point set having corner point it can also work out a satisfied result.