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

arXiv: https://arxiv.org/abs/1905.08748 · HTML: https://mlsanity.com/paper/1905.08748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 1 of 1)

- [RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds](https://mlsanity.com/paper/2205.01570/) (2022)

## Abstract (arXiv)

This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud. The point cloud is turned into a 2D range-image by exploiting the topology of the sensor. This image is then used as input to a U-net. This architecture has already proved its efficiency for the task of semantic segmentation of medical images. We demonstrate how it can also be used for the accurate semantic segmentation of a 3D LiDAR point cloud and how it represents a valid bridge between image processing and 3D point cloud processing. Our model is trained on range-images built from KITTI 3D object detection dataset. Experiments show that RIU-Net, despite being very simple, offers results that are comparable to the state-of-the-art of range-image based methods. Finally, we demonstrate that this architecture is able to operate at 90fps on a single GPU, which enables deployment for real-time segmentation.