Intelligent 3D world building from mobile terrestrial LiDAR point clouds

移动的激光扫描（MLS）可提供快速、高效且经济的数据收集。正如该技术的各种早期采用者所进行的研究所强调的那样，使用移动的激光扫描可以将现场时间减少80-90%，但将后台处理增加200- 300%。加工挑战来自于难以从大量激光点中区分不同的特征类型。即使可以应用一些有效的数值算法来加速3D点云处理，它们通常依赖于用户的显著交互。MLS工作流程中的重要“增值”在于将3D地理参考点云转换为城市基础设施和环境的智能3D模型。这种类型的语义描述不仅比原始数据更紧凑，而且它支持高级任务，如空间使用分析或翻新规划。这是该领域目前面临的真实的挑战。

Mobile laser scanning (MLS) provides fast, efficient and cost-effective data collection. As studies carried out by various early adopters of this technology have highlighted, using mobile laser scanning allows cutting down field times by 80-90% but increases back office processing by 200-300%. The processing challenge comes from the difficulty of distinguishing different feature types from the huge amount of laser points. Even if some efficient numerical algorithms can be applied to speed up the 3D point cloud processing, they often rely on significant interaction of the user. The significant "value add" in the MLS workflow comes in transforming 3D georeferenced point clouds into intelligent 3D models of our urban infrastructure and environment. Not only is this type of semantic description significantly more compact than the raw data, but it supports high-level tasks like analysis of space usage or planning for renovations. This is the real challenge currently facing this field.