基于局部重建的点云特征点提取

Feature Detection on Point Cloud via Local Reconstruction

为了有效地提取点云数据中的特征信息,针对采自分片光滑曲面的散乱点云数据,提出一种基于局部重建的鲁棒特征点提取方法.首先基于局部邻域的协方差分析计算每个数据点的特征度量,并通过阈值过滤获取初始特征点集合;然后在每个初始特征点的局部邻域内构建不跨越特征区域,以反映该点局部特征信息的三角形集合;再利用共享近邻算法对构造的三角形法向进行聚类,得到对应局部区域数据点的分类集合;最后对每一类点集拟合平面,通过判断该点是否同时落在多个平面来进行特征点提取.实验结果表明,该方法简单、稳定,对局部邻域选取的大小不敏感,具有一定的抗噪能力;能够在有效提取显著特征的同时,尽可能多地保留相对较弱的特征.

To extract sharp features from scattered point cloud sampled from piecewise smooth surfaces, a robust feature detection method using local reconstruction is proposed in this paper. First, for each point, a weight which measures the feature likelihood of a point is assigned according to a covariance analysis on its local neighborhood. By threshold filtering, the initial feature points are detected. Then, in the local neighborhood of each initial feature point, a triangle set is constructed, which effectively reflects the local feature structure. Subsequently, by applying the shared nearest neighbor clustering algorithm on the normal of triangles, we can obtain the clusters of points in the local neighborhood. Finally, for points of each cluster, one plane is fitted. Based on the fitted plane, the initial feature point is further identified as a true feature point, if it is nearly locating on the intersection of multiple fitting planes. Experimental results show that our method is simple, stable and insensitive to the size of selected neighborhood and robust to noise.