# LiDAR Enhanced Structure-from-Motion

arXiv: https://arxiv.org/abs/1911.03369 · HTML: https://mlsanity.com/paper/1911.03369/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

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

- [ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images](https://mlsanity.com/paper/2103.07798/) (2021)
- [Tightly-coupled Visual-DVL-Inertial Odometry for Robot-based Ice-water Boundary Exploration](https://mlsanity.com/paper/2303.17005/) (2023)
- [Lidar-Monocular Surface Reconstruction Using Line Segments](https://mlsanity.com/paper/2104.02761/) (2021)
- [PRISM: Color-Stratified Point Cloud Sampling](https://mlsanity.com/paper/2601.06839/) (2026)
- [JVLDLoc: a Joint Optimization of Visual-LiDAR Constraints and Direction Priors for Localization in Driving Scenario](https://mlsanity.com/paper/2208.09777/) (2022)
- [3D Reconstruction by Looking: Instantaneous Blind Spot Detector for Indoor SLAM through Mixed Reality](https://mlsanity.com/paper/2411.12514/) (2024)

## Abstract (arXiv)

Although Structure-from-Motion (SfM) as a maturing technique has been widely used in many applications, state-of-the-art SfM algorithms are still not robust enough in certain situations. For example, images for inspection purposes are often taken in close distance to obtain detailed textures, which will result in less overlap between images and thus decrease the accuracy of estimated motion. In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes data from a rotating LiDAR and a stereo camera pair to estimate sensor motions. We show that incorporating LiDAR helps to effectively reject falsely matched images and significantly improve the model consistency in large-scale environments. Experiments are conducted in different environments to test the performance of the proposed pipeline and comparison results with the state-of-the-art SfM algorithms are reported.