点云模型基于几何特征增强与保特征曲面拟合的法向估计方法  

The Normal Estimation Method of Point Cloud Model Based on Geometric Feature Enhancement and Feature-Preserving Surface Fitting

作　　者：李楠楠[1] 闫旭[1] 江栋 周骏 刘玉丽 Li Nannan;Yan Xu;Jiang Dong;Zhou Jun;Liu Yuli(College of Information Science and Technology,Dalian Maritime University,Dalian 116000)

机构地区：[1]大连海事大学信息科学与技术学院,大连116000

出　　处：《计算机辅助设计与图形学学报》2023年第3期392-404,共13页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61802045);北京航空航天大学开放课题项目(VRLAB2020A04).

摘　　要：现有法向估计中,尤其是模型中存在较大噪声的情况下,目标点邻域的选择是一个关键且困难的问题.针对点云模型,为了提高法向估计准确度,提出一种自适应选择邻域且保特征、抗噪声的法向估计方法.首先,提出双边非局部特征增强模块,根据网络前置学习特征以及邻域点几何特性对点邻域进行加权选择,并据此对局部特征进行增强,以提升网络对模型局部几何特征学习的能力;然后,采用局部特征与全局特征相结合的形式刻画点云完备的几何特征,并以此为基础进行局部曲面拟合及法向估计;最后,在局部曲面拟合中提出邻域保特征损失,依据邻域点受噪声干扰度对邻域点的拟合权重进行调整,实现保细节特征的局部曲面拟合,提高对噪声的鲁棒性.实验使用PCPNET数据集进行模型训练和测试,大量定性与定量的实验结果表明,与相关方法相比,所提方法对于不同噪声级别以及不同密度分布等复杂情形都可取得更加准确的法向估计结果,并更好地推动曲面重建等点云处理应用.From the existing normal estimation studies,how to select a proper neighbor of the target point is a key and difficult problem,especially when there exists high level of noises in the target point cloud.In order to improve the accuracy of the normal estimation,a new network architecture is proposed to estimate the normal of the point clouds,which can adaptively selects the neighbors,keeps the features and resists the noises.First,a new bilateral non-local feature enhancement module is introduced to improve the network’s ability to learn the local geometric features of the model,which can adaptively selects the neighbors according to the pre-learning features of the network and the geometric characteristics of the neighbors and then enhances the local features.Then,the combination of local features and global features is used to describe the complete geometric features of the point cloud,based on which further local surface fitting and normal estimation are carried out.Finally,in the local surface fitting,a feature-preserving neighbor loss is proposed,which can adjust the fitting weight of the neighboring points according to the degree of noise interference of the neighboring points,to realize the local surface fitting with the feature of detail preservation and improve the robustness of the method to noises.The PCPNET dataset is used to train and test the models.From extensive experiments,compared with the most related state-of-the-arts,proposed method gets better normal estimations results for the different noise levels and density distributions of the point clouds compared with existing methods,and it can also promote surface reconstruction and other point cloud processing tasks.

关 键 词：法向估计 曲面拟合 注意力机制 邻域选择 特征增强 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]