融合颜色信息和多尺度几何特征的点云语义分割方法  

Integrating Color Information and Multi-Scale Geometric Features for Point Cloud Semantic Segmentation

作　　者：张华[1] 徐瑞政 郑南山[1] 郝明[1] 刘东烈 史文中[2] ZHANG Hua;XU Ruizheng;ZHENG Nanshan;HAO Ming;LIU Donglie;SHI Wenzhong(School of Environment and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,China;Department of Land Surveying and Geo-informatics,The Hong Kong Polytechnic University,Hong Kong 999077,China;Geomatics Center of Guizhou Province,Guiyang 550004,China)

机构地区：[1]中国矿业大学环境与测绘学院,徐州221116 [2]香港理工大学土地测量及地理资讯学系,中国香港999077 [3]贵州省基础地理信息中心,贵阳550004

出　　处：《地球信息科学学报》2024年第6期1562-1575,共14页Journal of Geo-information Science

基　　金：国家自然科学基金项目(U22A20569)。

摘　　要：大规模室外点云具有丰富的空间结构,是地理信息获取重要手段之一,由于其本身具有不规则性、复杂几何结构特征及地物尺度变化大等特征,点云分割的准确性依然是一个巨大的挑战。特别是目前大规模点云几何信息及颜色等信息利用不充分等问题,为解决这些问题,本文提出了一种融合颜色信息和多尺度几何特征的点云语义分割方法(Integrating Color Information and Multi-Scale Geometric Features for Point Cloud Semantic Segmentation(CMGF-Net))。该方法中,分别设计了几何特征信息提取和语义特征信息提取模块。在几何特征信息提取模块中,为了充分利用点云数据的几何特征信息,设计了2个特征提取模块,分别是局部邻域的相对位置特征提取模块(RPF)和局部邻域的几何属性提取模块(LGP)。其中,RPF模块利用三维点云的空间法向信息以及相对空间距离,提取邻域点与当前点的相对位置关系;LGP模块利用点云几何属性在不同地物上有独特的表现特性,融合局部区域的几何属性特征;然后通过所设计的几何特征融合模块(LGF)将RPF模块和LGP模块所提取的特征信息进行融合得到融合后的几何特征信息。此外,为了从点云中学习到多尺度的几何特征,CMGF-Net在不同尺度的网络层中都进行了几何特征的提取,最终将所提取的几何特征与基于颜色特征提取的语义特征信息分层进行融合,以提高网络的学习能力。实验结果表明所提出的网络模型在Semantic3D数据集上的平均交并比(mIoU)和平均准确率(OA)达到了78.2%和95.0%,相较于KP Conv提高了3.6%和2.1%;在Sensat Urban数据集上达到了59.2%和93.7%,由此可见本文所提出的网络模型CMGF-Net在大规模室外场景点云分割具有较好的结果。Large outdoor point clouds have rich spatial structures and are one of the important means of obtaining geographic information.They have broad application prospects in fields such as autonomous driving,robot navigation,and 3D reconstruction.Due to its inherent irregularity,complex geometric structural features,and significant changes in land scale,the accuracy of point cloud segmentation remains a huge challenge.At present,most point cloud segmentation methods only extract features based on the original 3D coordinates and color information of point cloud data and have not fully explored the information contained in point cloud data with rich spatial information,especially the problem of insufficient utilization of geometric and color information in large-scale point clouds.In order to effectively address the aforementioned issues,this paper introduces the CMGF-Net,a method for semantic segmentation of point clouds that effectively integrates color information and multi-scale geometric features.In this network,dedicated modules are designed for extracting geometric feature information and semantic feature information.In the geometric feature information extraction path,to fully leverage the geometric characteristics of point cloud data,two feature extraction modules are designed:the Relative Position Feature(RPF)extraction module and the Local Geometry Properties(LGP)extraction module,both focusing on the characteristics of the local neighborhood.In the RPF module,spatial normal information of the 3D point cloud and relative spatial distances are utilized to extract the relative positional relationships between neighboring points and the central point.The LGP module exploits the unique performance characteristics of point cloud geometric properties across different terrains,integrating geometric attribute features from the local region.Subsequently,the designed Local Geometric Feature Fusion module(LGF)combines the extracted feature information from the RPF and LGP modules,yielding fused geometric feature informat

关 键 词：大规模点云 语义分割 法向量 几何属性 相对位置关系 多尺度特征融合 几何特征提取 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]