# Robust Building-based Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

arXiv: https://arxiv.org/abs/1904.03668 · HTML: https://mlsanity.com/paper/1904.03668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2019.

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

- [StarNet: Style-Aware 3D Point Cloud Generation](https://mlsanity.com/paper/2303.15805/) (2023)

## Abstract (arXiv)

The motivation of this paper is to address the problem of registering airborne LiDAR data and optical aerial or satellite imagery acquired from different platforms, at different times, with different points of view and levels of detail. In this paper, we present a robust registration method based on building regions, which are extracted from optical images using mean shift segmentation, and from LiDAR data using a 3D point cloud filtering process. The matching of the extracted building segments is then carried out using Graph Transformation Matching (GTM) which allows to determine a common pattern of relative positions of segment centers. Thanks to this registration, the relative shifts between the data sets are significantly reduced, which enables a subsequent fine registration and a resulting high-quality data fusion.