棱边特征曲面拓扑逼近重建算法

针对含有棱边特征的曲面模型难以正确重建这一问题,提出一种基于网格曲面延拓求交重建棱边特征区域的算法.首先对点云进行邻域高斯映射聚类分析,剔除棱边特征点,对剩余点云以种子点增长算法实现平坦连通区域的分割;然后将增益优化后的边界样点邻域点集作为曲面局部样本,采用三次Bézier曲线延伸方向为制导对点云进行扩展,提高曲面延拓区域的光滑性;最后对延拓后的平坦区域重建结果进行求交,采用曲面裁剪的方法重建棱边特征.以斯坦福大学提供的采样点云作为曲面重建数据,实验结果表明,在重建含有棱边特征曲面的过程中,该算法可有效地避免孔洞与棱边凹痕等错误的出现,且对非均匀采样数据具有良好的适应性.

Topological Approximation Reconstruction of Sharp Feature Surfaces

Aiming at the problem that the surfaces with some edge features are difficult to reconstruct correctly, a method for reconstruction of sharp feature surfaces is proposed based on intersection of meshes extension. Firstly, sharp feature points are removed by Gauss map clustering, and the remaining points are segmented by seed point growth algorithm. Then, to improve the smoothness of surfaces continuation areas, the bound- ary sample points after gain optimization are taken as the local sample of surfaces, and the points are ex- tended with the extended direction of the cubic Bézier curve as the guidance. Finally, the reconstruction re- sults of the extended flat areas are intersected, and sharp features are reconstructed by the method of surfaces trimming. The experimental data of surface reconstruction from the sampling points provided by Stanford University, the results show that this method can effectively avoid the occurrence of errors such as holes and edge notches, and has good adaptability to non-uniform sampling data.