# Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1703.03613 · HTML: https://mlsanity.com/paper/1703.03613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2017.

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

- [Efficient fine-grained road segmentation using superpixel-based CNN and CRF models](https://mlsanity.com/paper/2207.02844/) (2022)

## Abstract (arXiv)

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications.