基于群体智能的三维碎片全局最优匹配方法

Global optimal matching method for 3D fragments based on swarm intelligence

针对传统三维碎片整体匹配过程中误差积累的问题,提出了一种基于群体智能的全局最优匹配方法。该方法对破碎物体的三维多碎片全局匹配建立全局整体碎片匹配的数学模型,将碎片的整体最优匹配求解问题转换为求满足一定约束条件的最优匹配矩阵的组合优化问题,通过将自然社会认知优化算法进行离散化来求解该NP问题。典型实例分析验证了所提方法全局优化能力强,与初始位置无关,有较强的鲁棒性,为三维碎片整体匹配提供一个有效的方法。

Aiming at the error accumulation problem in the process of the traditional global matching of the threedimensional（ 3D） models, a global optimal matching method based on swarm intelligences was proposed. The global matching process for multiple 3D fragments was abstracted, and then a mathematic model of the global optimal matching was set up, the solution of the optimal matching for multiple 3D fragments was converted to satisfy certain constraint conditions of the optimal match matrix of combinatorial optimization problem. A discretization algorithm based on hybrid social cognitive optimization algorithm was proposed to solve the NP（ Non-deterministic Polynomial） problem. Finally, the classical example analyses verified that the proposed algorithm has global optimization ability and strong robustness without the initial position, and it provides an efficient method for global matching of the 3D fragments.