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

Source: [RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud](<https://www.sota2.com/research/paper/68fa65de1e209131cea0cf87>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:37:37.790Z

Authors: Pierre Biasutti, Aur\\'elie Bugeau, Jean-Fran\\c{c}ois Aujol, Mathieu Br\\'edif

Published: 2019-05-21T16:51:35.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1905.08748>)

[PDF](<https://arxiv.org/pdf/1905.08748.pdf>)