Abstract
We present an algorithm for explicit change detec-
tion on 3D point cloud data from a mobile mapping
scenario, namely the KITTI dataset. Our method is
able to partition a 3D point cloud into static and
dynamic points using ray traversal of a 3D voxel
grid. We are thus not using a machine learning
approach or RGB camera data but instead compute the
intersections of the scene volume with the
lines-of-sight between the sensor and the measured
points. Our approach does thus not require any
object detection or tracking and has comparatively
low requirements on the hardware. While our earlier
work focused on dense point clouds from terrestrial
3D laser scans, here we investigate its application
on the sparse 3D point clouds produced by a Velodyne
laser range finder in a mobile mapping scenario and
compare our results to two competing implementations
using the ground truth annotation from FuseMODNet
for a quantitative analysis. We also introduce
spherical quadtree point cloud reduction as a way to
only work on less than 1\% of the original data,
making processing multiple times faster while at the
same time producing results with equivalent $F_1$
scores.
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