  In recent years considerable research in LiDAR semantic segmentation was
conducted, introducing several new state of the art models. However, most
research focuses on single-scan point clouds, limiting performance especially
in long distance outdoor scenarios, by omitting time-sequential information.
Moreover, varying-density and occlusions constitute significant challenges in
single-scan approaches. In this paper we propose a LiDAR point cloud
preprocessing and postprocessing method. This multi-stage approach, in
conjunction with state of the art models in a multi-scan setting, aims to solve
those challenges. We demonstrate the benefits of our method through
quantitative evaluation with the given models in single-scan settings. In
particular, we achieve significant improvements in mIoU performance of over 5
percentage point in medium range and over 10 percentage point in far range.
This is essential for 3D semantic scene understanding in long distance as well
as for applications where offline processing is permissible.
