# CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data

arXiv: https://arxiv.org/abs/1709.02128 · HTML: https://mlsanity.com/paper/1709.02128/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 3 of 3)

- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [FugSeg: Fast Uncertainty-aware Ground Segmentation for 3D Point Cloud](https://mlsanity.com/paper/2605.08952/) (2026)
- [On Enhancing Ground Surface Detection from Sparse Lidar Point Cloud](https://mlsanity.com/paper/2105.11649/) (2021)

## Abstract (arXiv)

This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose approach is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the horizontal axis corresponds to the rotation angle and the vertical axis the indexes channels (i.e. laser beams). Multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and evaluated using a manually annotated dataset we prepared. The results show significant improvement of performance over the state-of-the-art method by Zhang et al. in terms of speed and also minor improvements in terms of accuracy.