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.

本文提出了一种多阶段的 LiDAR 点云预处理和后处理方法，在多扫描设置中结合最先进的模型，旨在解决单次扫描中的挑战，通过对给定模型在单次扫描设置中进行定量评估，我们展示了我们的方法的好处，在中距离和远距离上，mIoU 性能显著提升超过 5 个百分点和 10 个百分点，这对于长距离的三维语义场景理解和离线处理允许的应用至关重要。