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本文为荷兰乌得勒支大学（作者：Morten Asscheman）的硕士论文，共65页。

点云在地理信息系统GIS中是非常有价值的，可以用来从环境中提取多种信息。然而，未处理的点云有两个主要缺点：它们在可视化方面不是很有效，并且很难在视觉上区分不同的目标。本文提出了一种在铁路环境下自动分割和重构点云内目标的方法。为了实现更有效的可视化和更好的可识别性，用多边形网格替换各种铁路目标，并用Phong阴影模型渲染。与采用八叉树的原始点云相比，结果显示内存使用和平均渲染成本都减少了95%以上，并且提高了目标之间的可区分性。

Point clouds are very valuable in GIS and can be used to extract many kindsof information from an environment. However, there are two main shortcomings ofunprocessed point clouds: they are not very efficient in visualizations, and itis hard to visually discern between objects. This thesis presents an automaticmethod for segmenting and reconstructing objects inside point clouds in thecontext of railway environments. To achieve a more efficient visualization andbetter discernibility, various railway objects are replaced by polygon meshesand rendered with a Phong shading model. Compared to the original point cloudusing an octree, the results show a reduction of more than 95% in both memoryusage and average rendering costs as well as an improvement in thediscernibility between objects.

1 引言
2 分割与重建算法
3 研究结果与讨论
4 结论

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