几何特征引导的物体点云模型多层级分割  

Geometric feature guided multi-level segmentation for object point clouds

作　　者：刘妍 熊游依 韩妙妙 杨龙[1] LIU Yan;XIONG You-yi;HAN Miao-miao;YANG Long(College of Information Engineering,Northwest A&F University,Yangling Shaanxi 712100,China)

机构地区：[1]西北农林科技大学信息工程学院,陕西杨凌712100

出　　处：《图学学报》2023年第4期755-763,共9页Journal of Graphics

基　　金：国家自然科学基金项目(61702422);陕西省自然科学基础研究计划项目(2022JM-378)。

摘　　要：扫描物体点云的大量出现使得点云表面形状分析成为计算机图形学领域的研究热点。结构提取、形状编辑以及人-物交互等许多任务都需要尽可能精细的点云部件分割。由于浅层几何特征不易提取、形状缺失和噪声干扰等问题,扫描点云模型的部件分割(尤其细小部件实例检测)相对困难。因此,提出一种几何特征引导的物体点云模型多层级部件实例分割方法,根据点云表面局部弯曲程度提取凹、凸和边界特征,首先沿全局最显著凹线分割模型大致结构,再筛选几何特征差异明显的片段根据局部较浅特征进行细分,最后针对部分片段进行凹-凸协同分割,得到多层级分割结果。沿着由深到浅的几何特征,进行由粗到细的三次分割,能够更好地兼顾尺度不一的部件,实现了更细粒度的部件实例分割。实验结果表明,该方法在真实物体扫描点云和CAD物体模型采样点云上达到了较理想的分割效果,为真实物体点云部件分割提供了更精细且有效的方法。The proliferation of scanning object point clouds has brought surface shape analysis to the forefront of research in the computer graphics community.Tasks such as structure extraction,shape editing,and human-object interaction necessitate as precise a point cloud part segmentation as possible.However,due to the complexities of shallow geometric feature extraction,shape loss,and noise interference,the part segmentation,especially the small instance part extraction for scanning point clouds,remains relatively difficult.To address this issue,a multi-level part instance segmentation method guided by geometric features was proposed for object point clouds.The concave,convex,and boundary features were extracted based on the local bending extent of the point cloud surface.Firstly,our method segmented the general structure along the most global prominent concave line.Then it subdivided those segments with obvious geometric differences according to the local shallow features.Finally,the method performed concave-convex collaborative segmentation for some segments to obtain the multi-level segmentation results.The three-stage segmentation process from coarse to fine,along the geometric features from deep to shallow,allowed for better consideration of parts with different scales and finer grained part instance segmentation.The experimental results demonstrated that the proposed method could achieve superior segmentation results on both scanning point clouds and sampled point clouds from CAD models.It provided a more precise and effective method for part segmentation of scanning object point clouds.

关 键 词：点云模型 三维表面分割 特征检测 凹凸性 区域生长 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]