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Removing non-static objects from 3D laser scan data
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2018-06-19 , DOI: 10.1016/j.isprsjprs.2018.05.019
Johannes Schauer , Andreas Nüchter

For the purpose of visualization and further post-processing of 3D point cloud data, it is often desirable to remove moving objects from a given data set. Common examples for these moving objects are pedestrians, bicycles and motor vehicles in outdoor scans or manufactured goods and employees in indoor scans of factories. We present a new change detection method which is able to partition the points of multiple registered 3D scans into two sets: points belonging to stationary (static) objects and points belonging to moving (dynamic) objects. Our approach does not require any object detection or tracking the movement of objects over time. Instead, we traverse a voxel grid to find differences in volumetric occupancy for “explicit” change detection. Our main contribution is the introduction of the concept of “point shadows” and how to efficiently compute them. Without them, using voxel grids for explicit change detection is known to suffer from a high number of false positives when applied to terrestrial scan data. Our solution achieves similar quantitative results in terms of F1-score as competing methods while at the same time being faster.



中文翻译:

从3D激光扫描数据中删除非静态对象

为了可视化和进一步对3D点云数据进行后处理,通常需要从给定的数据集中删除移动的对象。这些移动物体的常见示例是室外扫描中的行人,自行车和机动车辆,或工厂室内扫描中的制成品和员工。我们提出了一种新的变更检测方法,该方法能够将多个已注册3D扫描的点划分为两组:属于固定(静态)对象的点和属于移动(动态)对象的点。我们的方法不需要任何物体检测或跟踪物体随时间的运动。取而代之的是,我们遍历体素网格以发现“显式”变化检测的体积占用率差异。我们的主要贡献是引入了“点阴影”的概念以及如何有效地计算它们。没有它们,使用体素栅格进行显式变化检测时,已知将其应用于地面扫描数据时会遭受大量误报。我们的解决方案在F方面获得了相似的定量结果1分作为竞争方法,同时更快。

更新日期:2018-06-19
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