基于局部样本增益优化的α-shape曲面拓扑重建

在曲面重建中,提高棱边特征重建精度是逆向工程和计算机辅助设计制造等领域的难点问题。采用样点的近似拓扑近邻点集作为曲面局部样本,对α-shape算法进行优化,使α-shape尺度阈值能更为准确地反映样点分布密度,从而提高α-shape曲面拓扑重建结果的正确性。样点的近似拓扑近邻点集的获取本质上是欧氏近邻点集的增益优化,使后者向邻近的稀疏区域适度延伸,从而弥补因数据分布不均匀而导致的拓扑邻域信息缺失。基于增益优化后的样点近邻点集并结合曲面重建先验知识可确定α-shape尺度阈值,使α-shape曲面拓扑重建过程中尺度阈值可自适应调整。试验表明:该算法使所得网格曲面基本不含孔洞和棱边凹痕,能更好保持棱边特征的形位精度,可减少初次过滤结果中的非流形面片,同时具有与主流Delaunay网格过滤算法相近的重建效率。

Surface Reconstruction withα-shape Based on Optimization of Surface Local Sample

The exact reconsctruction of sharp features is a difficult problem concerned by reverse engineering, computer aided design and manufacture. To optimize theα-shape algorithm, our algorithm uses approximation of the topological neighbors of a sample point as the surface local sample, which makesα-shape scale thresholds reflect density of the points better, then the validity of the surface reconstruction is improved. Gaining the approximation of the topological neighbors of a sample point is essentially to achieve gain optimization for the Euclidean neighbors of the point, which extends the latter toward the sparse region of the sampled data so that it decreeses dropping of the topological neighbors caused by non-uniform points. Based on the approximation of the topological neighbors of points and prior knowledge of surface reconstruction, anα-shapescale threshold corresponding to an triangular face could be calculated, so that the scale thresholds used in surface reconstruction could be adjusted by itself adaptively. The tests show that this algorithm can reconstruct non-uniform point set with few holes and edge hollows, better maintain the accuracy of form and position, and reduce non-manifold facets, meanwhile, its efficiency is comparable with mainstream algorithms.