We present an algorithm for explicit change detection 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 F₁ scores.