The key job here in the presented work is to investigate the performance of Generalized Ant Colony Optimizer (GACO) model in order to evolve the shape of three dimensional free-form Non Uniform Rational B-Spline (NURBS) curve using stereo (two) views. GACO model is a blend of two well known meta-heuristic optimization algorithms known as Simple Ant Colony and Global Ant Colony Optimization algorithms. Basically, the work talks about the solution of NURBS-fitting based reconstruction process. Therefore, GACO model is used to optimize the NURBS parameters (control points and weights) by minimizing the weighted least-square errors between the data points and the fitted NURBS curve. The algorithm is applied by first assuming some pre-fixed values of NURBS parameters. The experiments clearly show that the optimization procedure is a better option in a case where good initial locations of parameters are selected. A detailed experimental analysis is given in support of our algorithm. The implemented error analysis shows that the proposed methodology perform better as compared to the conventional methods.

在目前的工作中，这里的关键工作是研究广义蚁群优化器 (GACO) 模型的性能，以便使用立体 (两个) 视图演化三维自由形式非均匀有理 B 样条 (NURBS) 曲线的形状. GACO 模型混合了两种众所周知的元启发式优化算法，称为简单蚁群和全局蚁群优化算法。基本上，该工作讨论了基于 NURBS 拟合的重建过程的解决方案。因此，GACO 模型用于通过最小化数据点和拟合 NURBS 曲线之间的加权最小二乘误差来优化 NURBS 参数（控制点和权重）。通过首先假设 NURBS 参数的一些预先确定的值来应用该算法。实验清楚地表明，在选择了良好的参数初始位置的情况下，优化程序是更好的选择。给出了详细的实验分析来支持我们的算法。实施的误差分析表明，与传统方法相比，所提出的方法性能更好。