# Automatic labelling of urban point clouds using data fusion

arXiv: https://arxiv.org/abs/2108.13757 · HTML: https://mlsanity.com/paper/2108.13757/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways](https://mlsanity.com/paper/2003.08284/) (2020)
- [Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification](https://mlsanity.com/paper/1712.00032/) (2017)

## Built on by (most cited first, 6 of 6)

- [Learning Moving-Object Tracking with FMCW LiDAR](https://mlsanity.com/paper/2203.00959/) (2022)
- [Towards Robust Few-shot Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2309.11228/) (2023)
- [Determining Accessible Sidewalk Width by Extracting Obstacle Information from Point Clouds](https://mlsanity.com/paper/2211.04108/) (2022)
- [You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning](https://mlsanity.com/paper/2211.09302/) (2022)
- [From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation](https://mlsanity.com/paper/2302.03114/) (2023)
- [Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks](https://mlsanity.com/paper/2309.09206/) (2023)

## Abstract (arXiv)

In this paper we describe an approach to semi-automatically create a labelled dataset for semantic segmentation of urban street-level point clouds. We use data fusion techniques using public data sources such as elevation data and large-scale topographical maps to automatically label parts of the point cloud, after which only limited human effort is needed to check the results and make amendments where needed. This drastically limits the time needed to create a labelled dataset that is extensive enough to train deep semantic segmentation models. We apply our method to point clouds of the Amsterdam region, and successfully train a RandLA-Net semantic segmentation model on the labelled dataset. These results demonstrate the potential of smart data fusion and semantic segmentation for the future of smart city planning and management.