激光三维扫描技术可快速获取扫描目标表面的点云数据，包括用于描述目标几何特征的空间点坐标和刻画目标材质反射率信息的激光反射强度。将激光三维点云的自动语义分割技术应用于地质勘探研究中，能为区域地质特征描绘奠定基础。为了展示激光三维扫描技术在地质场景大规模语义分割领域的最新进展，首先对摄影测量和激光雷达两种三维点云获取方式进行了比较，得到激光雷达在精度、泛用性、不易受光照条件影响等方面具有优势。通过阐述岩性语义分割的原理，将近年来基于几何特征或强度特征的岩性点云分割方法进行了全面的归纳和总结；介绍了常用大规模点云数据集和评价指标，并比较不同算法分割性能；最后总结了现有方法的局限性，并指出岩性语义分割任务未来研究方向进行展望。

Laser 3D scanning technology rapidly acquires point cloud data of target surfaces, including spatial point coordinates that describe the geometric features of the target and laser reflectance intensity that characterizes the material's reflectivity. The application of automatic semantic segmentation techniques for 3D point clouds in geological exploration research lays the foundation for depicting regional geological features. To demonstrate the recent advancements of 3D laser scanning technology in large-scale semantic segmentation within geological scenarios, firstly, photogrammetry and LiDAR as two methods for acquiring 3D point clouds were compared, highlighting the advantages of LiDAR in terms of accuracy, versatility, and insensitivity to lighting conditions. By elucidating the principles of lithological semantic segmentation, a comprehensive review and summary of recent methods based on geometric or intensity features were provided. Common large-scale point cloud datasets and evaluation metrics were introduced, and the segmentation performance of different algorithms was compared. Finally, the limitations of existing methods were summarized, and future research directions for lithological semantic segmentation tasks were outlined.