作　　者： ; ; ; ;

机构地区： 浙江工业大学机电工程学院

出　　处： 《工程图学学报》 2004年第1期71-75,共5页

摘　　要： 探讨了曲面密集三维散乱点数据的三角网格智能重建方法。建立了基于自组织特征映射神经网络的三角网格构建模型。该模型利用神经元对曲面散乱点的学习和训练来模拟曲面上的点与点之间的内在关系,结点连接权矢量集作为对散乱点集的工程近似化并重构曲面样本点的内在拓扑关系,实现曲面密集三维散乱点数据的自组织压缩。按六角形阵列侧抑制邻区训练调整网络神经元权重矢量,使网络输出层结点呈六角形阵列分布,可实现测量点集压缩后的Delaunay三角逼近剖分。计算机仿真实验表明,所建神经网络模型可以实现期望规模和精度的三角网格剖分并有效保持原数据点集的拓扑特征。 For the dense 3-D scattered points, a triangle mesh reconstruction approach based on the self-organizing feature map (SOFM) neural network is developed. The inherent topologic relations between the scattered points on the curved surface are learned by the SOFM neural network. The weight vectors of the neurons on the output layer of the neural network are used to approximate the dense 3-D scattered points, so the dense scattered points can be reduced to the reasonable scale, while the topologic feature of the whole scattered points are remained. The region of the lateral inhibition is hexagon within which the neuron weight vectors are adjusted according to the SOFM training algorithm. The neurons on the output layer are distributed in the array of hexagon after training, thereby the Delaunay triangulation in way of high approximation is realized. The computer simulation results show that this approach is satisfying.

关 键 词： 神经网络 三角形网格 逆向工程 散乱点 数据压缩 自组织压缩

领　　域： [自动化与计算机技术] [自动化与计算机技术]