# Fast 3D Line Segment Detection From Unorganized Point Cloud

arXiv: https://arxiv.org/abs/1901.02532 · HTML: https://mlsanity.com/paper/1901.02532/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2019.

## Built on by (most cited first, 10 of 10)

- [HoW-3D: Holistic 3D Wireframe Perception from a Single Image](https://mlsanity.com/paper/2208.06999/) (2022)
- [Fully Geometric Panoramic Localization](https://mlsanity.com/paper/2403.19904/) (2024)
- [SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud](https://mlsanity.com/paper/2208.01925/) (2022)
- [BoundED: Neural Boundary and Edge Detection in 3D Point Clouds via Local Neighborhood Statistics](https://mlsanity.com/paper/2210.13305/) (2022)
- [LDL: Line Distance Functions for Panoramic Localization](https://mlsanity.com/paper/2308.13989/) (2023)
- [Structured-Li-GS: Structured 3D Gaussians Splatting with LiDAR Incorporation and Spatial Constraints](https://mlsanity.com/paper/2606.27509/) (2026)
- [WLTCL: Wide Field-of-View 3-D LiDAR Truck Compartment Automatic Localization System](https://mlsanity.com/paper/2504.18870/) (2025)
- [LECalib: Line-Based Event Camera Calibration](https://mlsanity.com/paper/2512.22441/) (2025)
- [Detecting 3D Line Segments for 6DoF Pose Estimation with Limited Data](https://mlsanity.com/paper/2601.12090/) (2026)
- [RoEL: Robust Event-based 3D Line Reconstruction](https://mlsanity.com/paper/2602.18258/) (2026)

## Abstract (arXiv)

This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud. Unlike traditional methods which usually extract 3D edge points first and then link them to fit for 3D line segments, we propose a very simple 3D line segment detection algorithm based on point cloud segmentation and 2D line detection. Given the input unorganized point cloud, three steps are performed to detect 3D line segments. Firstly, the point cloud is segmented into 3D planes via region growing and region merging. Secondly, for each 3D plane, all the points belonging to it are projected onto the plane itself to form a 2D image, which is followed by 2D contour extraction and Least Square Fitting to get the 2D line segments. Those 2D line segments are then re-projected onto the 3D plane to get the corresponding 3D line segments. Finally, a post-processing procedure is proposed to eliminate outliers and merge adjacent 3D line segments. Experiments on several public datasets demonstrate the efficiency and robustness of our method. More results and the C++ source code of the proposed algorithm are publicly available at https://github.com/xiaohulugo/3DLineDetection.