基于差异性累积与子空间传播的法向估计算法

以分割为基础的法向估计算法主要是通过法向的差异来构造点之间的相似性.针对由于距离属性的缺失,使这类算法对于紧邻面及一些光滑曲面的估计结果并不理想的问题,提出基于差异性累积与子空间传播的法向估计算法,利用最短路将法向的差异性和点的位置信息相融合.首先,对于部分点的邻域,找到邻域点间的最短路,通过叠加最短路中点的法向差异,计算点之间的相似性;然后,利用谱分割对邻域进行分割,选择一子邻域估计此点的法向;最后,为了提高效率,提出法向约束的子空间结构传播算法,其余邻域的分割结果由已有的分割结果进行推断.在Fandisk等仿真数据和Armadillon等真实扫描数据上的实验结果表明,文中算法能准确地恢复模型的尖锐特征,有效地克服噪声及非均匀采样.

Point Cloud Normal Estimation via Differential Accumulation and Subspace Propagation

The normal estimation algorithms based on segmentation usually construct affinity matrix by the difference of initial normal. The results for thin sharp features and some smooth surfaces are not very well due to the absence of distance between points. This paper combines the difference of normal and the position information of points by the geodesic path. Firstly, for the neighborhoods of some points, we find the geodesic paths between the neighborhood points, and the similarity between two points is calculated by accumulating the difference of points’ initial normal on the path connecting them. Then, the spectral segmentation is used to segment each neighborhood, and one subneighborhood is selected to estimate the normal. Finally, in order to reduce the computational cost of the algorithm, a normal constrained subspace structure propagation algorithm is proposed, and the segmentation results of the remaining neighborhoods are inferred from the existing segmentation results. Experimental results on simulation and real scan data show that the proposed algorithm is capable of overcoming noise and anisotropic samplings, while preserving sharp features within the original point data.