# Method Towards CVPR 2021 Image Matching Challenge

arXiv: https://arxiv.org/abs/2108.04453 · HTML: https://mlsanity.com/paper/2108.04453/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 7 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [DISK: Learning local features with policy gradient](https://mlsanity.com/paper/2006.13566/) (2020)
- [MODS: Fast and Robust Method for Two-View Matching](https://mlsanity.com/paper/1503.02619/) (2015)

## Built on by (most cited first, 2 of 2)

- [MegLoc: A Robust and Accurate Visual Localization Pipeline](https://mlsanity.com/paper/2111.13063/) (2021)
- [Marginalized Bundle Adjustment: Multi-View Camera Pose from Monocular Depth Estimates](https://mlsanity.com/paper/2602.18906/) (2026)

## Abstract (arXiv)

This report describes Megvii-3D team's approach towards CVPR 2021 Image Matching Workshop.