逼近散乱点云数据的三角形网格精确剖分

基于自组织特征映射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，但该模型存在逼近误差和边缘误差。为减小三角形网格 的逼近误差和边缘误差，构建了精确逼近的三角形网格模型。首先采用整个测量点云，对三 角形网格模型中的所有神经元进行整体训练；然后对三角形网格中的网格神经元的位置权 重，沿网格顶点法矢方向进行修正；最后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。算例表明，应用该模型，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大幅提高并覆盖散乱点云整体分布范围。

Reconstruction of Triangle Mesh for Unorganized Point Cloud Data withReconstruction of Triangle Mesh for Unorganized Point Cloud Data with

An approach based on the self-organizing feature map (SOFM) neural network has been developed to reconstruct Delaunay triangle mesh for the unorganized measured point cloud. However the approach suffers from approximation and boundary problems. A triangle mesh model with high approximation precision is proposed in order to reduce the approximation error and boundary error. First all the neurons of the mesh model are trained directly over the unorganized point cloud. Next the neuron location weights of the mesh model are adjusted along the normal vectors of the mesh vertices. Last only the boundary neurons of the mesh model undergo training by the boundary points of the measured point cloud. As a result of applying the proposed mesh model, the boundary error is greatly reduced and the mesh is drawn toward the sampled object with higher precision comparing with the original SOFM training algorithm. The feasibility of the developed mesh model is demonstrated on two examples.