# PPGNet: Learning Point-Pair Graph for Line Segment Detection

arXiv: https://arxiv.org/abs/1905.03415 · HTML: https://mlsanity.com/paper/1905.03415/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2019.

## Built on by (most cited first, 30 of 39)

- [Line Segment Detection Using Transformers without Edges](https://mlsanity.com/paper/2101.01909/) (2021)
- [GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://mlsanity.com/paper/2304.02008/) (2023)
- [DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients](https://mlsanity.com/paper/2212.07766/) (2022)
- [SOLD2: Self-supervised Occlusion-aware Line Description and Detection](https://mlsanity.com/paper/2104.03362/) (2021)
- [3D Line Mapping Revisited](https://mlsanity.com/paper/2303.17504/) (2023)
- [Towards Light-weight and Real-time Line Segment Detection](https://mlsanity.com/paper/2106.00186/) (2021)
- [HEAT: Holistic Edge Attention Transformer for Structured Reconstruction](https://mlsanity.com/paper/2111.15143/) (2021)
- [Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning](https://mlsanity.com/paper/2210.12971/) (2022)
- [Fully Convolutional Line Parsing](https://mlsanity.com/paper/2104.11207/) (2021)
- [ELSD: Efficient Line Segment Detector and Descriptor](https://mlsanity.com/paper/2104.14205/) (2021)
- [Pose Refinement with Joint Optimization of Visual Points and Lines](https://mlsanity.com/paper/2110.03940/) (2021)
- [HoW-3D: Holistic 3D Wireframe Perception from a Single Image](https://mlsanity.com/paper/2208.06999/) (2022)
- [4D Gaussian Splatting SLAM](https://mlsanity.com/paper/2503.16710/) (2025)
- [Learning to Construct 3D Building Wireframes from 3D Line Clouds](https://mlsanity.com/paper/2208.11948/) (2022)
- [Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image](https://mlsanity.com/paper/2104.07986/) (2021)
- [PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images](https://mlsanity.com/paper/2204.07243/) (2022)
- [Structured Outdoor Architecture Reconstruction by Exploration and Classification](https://mlsanity.com/paper/2108.07990/) (2021)
- [Detecting Line Segments in Motion-blurred Images with Events](https://mlsanity.com/paper/2211.07365/) (2022)
- [A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges](https://mlsanity.com/paper/2305.00264/) (2023)
- [Hole-robust Wireframe Detection](https://mlsanity.com/paper/2111.15064/) (2021)
- [ScaleLSD: Scalable Deep Line Segment Detection Streamlined](https://mlsanity.com/paper/2506.09369/) (2025)
- [HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery](https://mlsanity.com/paper/2507.16251/) (2025)
- [LDL: Line Distance Functions for Panoramic Localization](https://mlsanity.com/paper/2308.13989/) (2023)
- [Parsing Line Segments of Floor Plan Images Using Graph Neural Networks](https://mlsanity.com/paper/2303.03851/) (2023)
- [Synthesizing Realistic Data for Table Recognition](https://mlsanity.com/paper/2404.11100/) (2024)
- [A Deep Learning Approach Based on Graphs to Detect Plantation Lines](https://mlsanity.com/paper/2102.03213/) (2021)
- [Sparse Optical Flow-Based Line Feature Tracking](https://mlsanity.com/paper/2204.03331/) (2022)
- [EVOLIN Benchmark: Evaluation of Line Detection and Association](https://mlsanity.com/paper/2303.05162/) (2023)
- [Optimized Vectorizing of Building Structures with Switch: High-Efficiency Convolutional Channel-Switch Hybridization Strategy](https://mlsanity.com/paper/2306.15035/) (2023)
- [Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking](https://mlsanity.com/paper/2502.17766/) (2025)

## Abstract (arXiv)

In this paper, we present a novel framework to detect line segments in man-made environments. Specifically, we propose to describe junctions, line segments and relationships between them with a simple graph, which is more structured and informative than end-point representation used in existing line segment detection methods. In order to extract a line segment graph from an image, we further introduce the PPGNet, a convolutional neural network that directly infers a graph from an image. We evaluate our method on published benchmarks including York Urban and Wireframe datasets. The results demonstrate that our method achieves satisfactory performance and generalizes well on all the benchmarks. The source code of our work is available at urlhttps://github.com/svip-lab/PPGNet.