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Removing non-static objects from 3D laser scan data
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AI
DOI：10.1016/j.isprsjprs.2018.05.019.png)
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摘要
En 中文
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 F,-score as competing methods while at the same time being faster.
Keyword:
3D point clouds
Change detection
Moving object detection
Voxel traversal
Post-processing
Sphere quadtree
Mobile mapping
Terrestrial scans
期刊
IF:
12.2
论文数:
4.5K
被引数:
3.2W
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