中文摘要：

针对L_1中值骨架提取方法存在迭代次数较多、相邻区域较紧密时骨架易跨越区域等问题,提出一种分区提取骨架的算法。结合点云区域的连通性及局部相关性,采用马尔科夫随机场模型,将给定点云分割成不同区域。在相同标号的区域根据区域大小和点集数自适应地计算不同的初始收缩邻域尺度,用L_1中值不断收缩迭代提取各区域的骨架分支,通过主成分分析及连接角判定骨架连接方式,并根据该连接方式将骨架分支连接成完整的点云骨架。实验结果表明,该算法能够自适应地提取点云骨架,减少点云收缩的迭代次数,保持模型原有的拓扑结构,对于含有区域紧密度不均匀的模型有较好的效果。

英文摘要：

In order to solve the problem that the L~ median skeleton extraction method involves many iterations and the skeleton easily crosses the region of the tight adjacent region, an algorithm for extracting skeleton after segmentation is proposed. According to the connectivity of the region of point cloud and the local correlation characteristics, the point cloud can be segmented into different regions using the Markov Random Field（MRF） model. Different initial contraction neighborhood scales are adaptively calculated in terms of region size and number of points in the same labeled region. The skeleton branches of each region are extracted by L~ median iteration. The skeleton connection is determined by Principal Component Analysis（PCA） and connection angle. Then the skeleton branch is connected to a complete point cloud skeleton according to the connection mode. Experimental results show that the algorithm can adaptively extract the skeleton of the points cloud and reduce the number of iterations to contract points cloud. It not only can keep the original topological structure of the model,but also has a good effect on the model with uneven region tightness.