点云重构三角网格的生长型神经气算法

From Point Cloud to Triangular Mesh by Growing Neural Gas

为克服点云噪声、不均匀分布和复杂拓扑结构对三角网格重构的限制,改进了生长型神经气重构算法.以样本在网格局部投影作为神经元插入判据,自适应调节网格增长速度,保持几何变换与拓扑变换的协调.利用非流形边检测机制删除冗余连接,保持网格的拓扑有效性.网络学习过程中动态更新三角片结构,且在孔洞修复阶段扩大近邻查找范围,连接近邻节点中的边界点,直到网格收敛,最终得到正确的欧拉示性数.算例表明,改进的算法对带噪声点云具有鲁棒性,可根据非均匀点云的分布自动调整网格密度,且能重构具有复杂拓扑结构的曲面.重构的三角网格对曲面逼近精度较高,网格出度均匀,三角形近似等边.

Triangular surface reconstruction out-of-point clouds suffer from noisy, non-uniform distributed data, and complicated topology structure. Thus, an improved growing neural gas approach is proposed. A point cloud projection on local grid is employed to direct node insertion; therefore, to adaptively control neuron growing rate, the geometric and topologic transforms are sychronized. Redundant links arc removed through non-manifold edge detection, that guarantees a topologically validate mesh. The network keeps updating triangular grid and then fills holes in a post phase by the extended neighborhood connection mechanism. After all those steps come to a convergent end, there is a gap free and an Euler characteristic correct mesh was obtained. Case studies invalidate the noise robustness and complex topology adaptability. The algorithm cand further adjust mesh size to point cloud distribution. Plus is that reconstructed mesh approximates the surface in high accuracy, and it characterizes uniform equilateral edge share.