# Attentional Graph Neural Network for Parking-slot Detection

arXiv: https://arxiv.org/abs/2104.02576 · HTML: https://mlsanity.com/paper/2104.02576/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation](https://mlsanity.com/paper/2007.11864/) (2020)
- [VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View](https://mlsanity.com/paper/1804.07027/) (2018)

## Built on by (most cited first, 9 of 9)

- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [ParkGaussian: Surround-view 3D Gaussian Splatting for Autonomous Parking](https://mlsanity.com/paper/2601.01386/) (2026)
- [SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving](https://mlsanity.com/paper/2302.12966/) (2023)
- [Enhanced Parking Perception by Multi-Task Fisheye Cross-view Transformers](https://mlsanity.com/paper/2408.12575/) (2024)
- [CNN-based Two-Stage Parking Slot Detection Using Region-Specific Multi-Scale Feature Extraction](https://mlsanity.com/paper/2108.06185/) (2021)
- [Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition](https://mlsanity.com/paper/2309.00168/) (2023)
- [Holistic Parking Slot Detection with Polygon-Shaped Representations](https://mlsanity.com/paper/2310.11629/) (2023)
- [LaB-CL: Localized and Balanced Contrastive Learning for improving parking slot detection](https://mlsanity.com/paper/2410.07832/) (2024)
- [Advancing Real-World Parking Slot Detection with Large-Scale Dataset and Semi-Supervised Baseline](https://mlsanity.com/paper/2509.13133/) (2025)

## Abstract (arXiv)

Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning the link information of the marking-points, resulting in complex post-processing and erroneous detection. In this paper, we propose an attentional graph neural network based parking-slot detection method, which refers the marking-points in an around-view image as graph-structured data and utilize graph neural network to aggregate the neighboring information between marking-points. Without any manually designed post-processing, the proposed method is end-to-end trainable. Extensive experiments have been conducted on public benchmark dataset, where the proposed method achieves state-of-the-art accuracy. Code is publicly available at urlhttps://github.com/Jiaolong/gcn-parking-slot.