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This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of apopular semantic segmentation network for the semantic segmentation of a 3DLiDAR point cloud. The point cloud is turned into a 2D range-image byexploiting the topology of the sensor. This image is then used as input to aU-net. This architecture has already proved its efficiency for the task ofsemantic segmentation of medical images. We demonstrate how it can also be usedfor the accurate semantic segmentation of a 3D LiDAR point cloud and how itrepresents a valid bridge between image processing and 3D point cloudprocessing. Our model is trained on range-images built from KITTI 3D objectdetection dataset. Experiments show that RIU-Net, despite being very simple,offers results that are comparable to the state-of-the-art of range-image basedmethods. Finally, we demonstrate that this architecture is able to operate at90fps on a single GPU, which enables deployment for real-time segmentation.

RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud