# GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

arXiv: https://arxiv.org/abs/1902.11046 · HTML: https://mlsanity.com/paper/1902.11046/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 19 of 19)

- [LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method](https://mlsanity.com/paper/2104.00099/) (2021)
- [ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization](https://mlsanity.com/paper/2203.03610/) (2022)
- [A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature](https://mlsanity.com/paper/2401.02542/) (2024)
- [Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework](https://mlsanity.com/paper/2108.12159/) (2021)
- [Knowledge Distillation for Feature Extraction in Underwater VSLAM](https://mlsanity.com/paper/2303.17981/) (2023)
- [DINO-VO: A Feature-based Visual Odometry Leveraging a Visual Foundation Model](https://mlsanity.com/paper/2507.13145/) (2025)
- [RWT-SLAM: Robust Visual SLAM for Highly Weak-textured Environments](https://mlsanity.com/paper/2207.03539/) (2022)
- [Light-SLAM: A Robust Deep-Learning Visual SLAM System Based on LightGlue under Challenging Lighting Conditions](https://mlsanity.com/paper/2407.02382/) (2024)
- [Learning Better Keypoints for Multi-Object 6DoF Pose Estimation](https://mlsanity.com/paper/2308.07827/) (2023)
- [Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Images](https://mlsanity.com/paper/2504.08253/) (2025)
- [A comparative evaluation of learned feature descriptors on hybrid monocular visual SLAM methods](https://mlsanity.com/paper/2104.00085/) (2021)
- [DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing](https://mlsanity.com/paper/2401.09160/) (2024)
- [Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection](https://mlsanity.com/paper/2605.22605/) (2026)
- [Accurate Visual-Inertial SLAM by Feature Re-identification](https://mlsanity.com/paper/2102.13438/) (2021)
- [An Error-Matching Exclusion Method for Accelerating Visual SLAM](https://mlsanity.com/paper/2402.14345/) (2024)
- [NeRF-Supervised Feature Point Detection and Description](https://mlsanity.com/paper/2403.08156/) (2024)
- [Black-box Adversarial Attacks on CNN-based SLAM Algorithms](https://mlsanity.com/paper/2505.24654/) (2025)
- [DL-VINS-Factory: A Modular Framework for Learned Visual Front-Ends in Visual-Inertial SLAM](https://mlsanity.com/paper/2607.01757/) (2026)
- [Desc++: Efficient Descriptor Enhancement for Data Association in Existing Visual SLAM Systems](https://mlsanity.com/paper/2607.11099/) (2026)

## Abstract (arXiv)

In this paper, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORB-SLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone.