# Semantic 3D Grid Maps for Autonomous Driving

arXiv: https://arxiv.org/abs/2211.01700 · HTML: https://mlsanity.com/paper/2211.01700/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2022.

## Built on by (most cited first, 5 of 5)

- [PIG: Prompt Images Guidance for Night-Time Scene Parsing](https://mlsanity.com/paper/2406.10531/) (2024)
- [Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted Data](https://mlsanity.com/paper/2306.07344/) (2023)
- [Addressing Data Annotation Challenges in Multiple Sensors: A Solution for Scania Collected Datasets](https://mlsanity.com/paper/2403.18649/) (2024)
- [SDS++: Online Situation-Aware Drivable Space Estimation for Automated Driving](https://mlsanity.com/paper/2406.01941/) (2024)
- [FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps](https://mlsanity.com/paper/2502.00395/) (2025)

## Abstract (arXiv)

Maps play a key role in rapidly developing area of autonomous driving. We survey the literature for different map representations and find that while the world is three-dimensional, it is common to rely on 2D map representations in order to meet real-time constraints. We believe that high levels of situation awareness require a 3D representation as well as the inclusion of semantic information. We demonstrate that our recently presented hierarchical 3D grid mapping framework UFOMap meets the real-time constraints. Furthermore, we show how it can be used to efficiently support more complex functions such as calculating the occluded parts of space and accumulating the output from a semantic segmentation network.