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

Source: [Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa633a1e209131ce9fd9de>)

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

Data updated: 2025-10-25T20:36:15.201Z

Authors: Luca Caltagirone, Samuel Scheidegger, Lennart Svensson, Mattias Wahde

Published: 2017-03-10T10:26:24.000Z

## Abstract

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.

## 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/1703.03613>)

[PDF](<https://arxiv.org/pdf/1703.03613.pdf>)