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

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 information. Furthermore, to learn multi- scale geometric features from the point cloud, CMGF- Net conducts geometric feature extraction at different scales within the network layers. Eventually, the extracted geometric features are hierarchically fused with semantically extracted features based on color information. By extracting multi-scale geometric features and integrating semantic features, the learning ability of the network is enhanced. The experimental results show that our proposed network model achieves a mean Intersection Over Union (mIoU) of 78.2% and an Overall Accuracy (OA) of 95.0% on the Semantic3D dataset, outperforming KPConv by 3.6% and 2.1%, respectively. On the SensatUrban dataset, it achieves a mIOU of 59.2% and an OA of 93.7%. These findings demonstrate that the proposed network model, CMGF-Net, yields promising results in the segmentation of large-scale outdoor point clouds. © 2024 Science Press. All rights reserved.