作　　者： ; ; ;

机构地区： 中国计量学院机电工程学院

出　　处： 《计算机工程与应用》 2011年第35期196-198,共3页

摘　　要： 基于SOFM神经网络构建的三角形网格模型可以实现测量点云压缩后的Delaunay三角逼近剖分,但该模型存在边缘误差。为减小三角形网格的边缘误差,改进了三角形网格模型的训练模式,提出了3步训练模式。第1步采用整个测量点云,对三角形网格模型中的所有神经元进行整体训练;第2步采用测量点云中的边界点集,对三角形网格模型中的网格边界神经元进行训练;第3步采用边界点集中的角点点集,对与边界角点匹配最佳的网格边界神经元进行训练。算例表明,应用该训练模式,可以有效减小三角形网格的边缘误差,三角形网格逼近散乱点云的逼近精度得到提高并覆盖散乱点云整体分布范围。 An approach based on the Self-Organizing Feature Map（SOFM） neural network has been developed to reconstruct triangle mesh for the unorganized digitized point cloud.However the approach suffers from boundary problems.A three-steps training method is proposed in order to reduce the boundary error.All the neurons of the mesh model are trained directly over the unroganized digitized point-cloud.Only the boundary neurons of the mesh model is undergo trained by the boundary points of the digitized point-cloud.Only the winner neurons with respect to the comer points are trained by the corner points of the boundary points.As a result of applying the proposed training method, the boundary error is greatly reduced and the mesh is drawn toward the sampled object with higher precision compared with the original SOFM training algorithm.The feasibility of the developed training method is demonstrated on two examples.

关 键 词： 逆向工程 三角形网格 神经网络 边缘误差 散乱点云

领　　域： [自动化与计算机技术] [自动化与计算机技术]