# MegLoc: A Robust and Accurate Visual Localization Pipeline

arXiv: https://arxiv.org/abs/2111.13063 · HTML: https://mlsanity.com/paper/2111.13063/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 19 of 19)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [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)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [Particular object retrieval with integral max-pooling of CNN activations](https://mlsanity.com/paper/1511.05879/) (2015)
- [ASLFeat: Learning Local Features of Accurate Shape and Localization](https://mlsanity.com/paper/2003.10071/) (2020)
- [Self-supervising Fine-grained Region Similarities for Large-scale Image Localization](https://mlsanity.com/paper/2006.03926/) (2020)
- [Robust Image Retrieval-based Visual Localization using Kapture](https://mlsanity.com/paper/2007.13867/) (2020)
- [4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving](https://mlsanity.com/paper/2009.06364/) (2020)
- [Method Towards CVPR 2021 Image Matching Challenge](https://mlsanity.com/paper/2108.04453/) (2021)

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

- [SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding](https://mlsanity.com/paper/2306.05407/) (2023)
- [Visual Localization using Imperfect 3D Models from the Internet](https://mlsanity.com/paper/2304.05947/) (2023)
- [One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation](https://mlsanity.com/paper/2211.15977/) (2022)
- [Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras](https://mlsanity.com/paper/2207.06058/) (2022)
- [Self-Supervised Learning for Place Representation Generalization across Appearance Changes](https://mlsanity.com/paper/2303.02370/) (2023)
- [Progressive Volume Distillation with Active Learning for Efficient NeRF Architecture Conversion](https://mlsanity.com/paper/2304.04012/) (2023)
- [Co-VisiON: Co-Visibility ReasONing on Sparse Image Sets of Indoor Scenes](https://mlsanity.com/paper/2506.16805/) (2025)
- [CyberLoc: Towards Accurate Long-term Visual Localization](https://mlsanity.com/paper/2301.02403/) (2023)
- [FUSELOC: Fusing Global and Local Descriptors to Disambiguate 2D-3D Matching in Visual Localization](https://mlsanity.com/paper/2408.12037/) (2024)
- [Robust Scene Coordinate Regression via Geometrically-Consistent Global Descriptors](https://mlsanity.com/paper/2512.17226/) (2025)

## Abstract (arXiv)

In this paper, we present a visual localization pipeline, namely MegLoc, for robust and accurate 6-DoF pose estimation under varying scenarios, including indoor and outdoor scenes, different time across a day, different seasons across a year, and even across years. MegLoc achieves state-of-the-art results on a range of challenging datasets, including winning the Outdoor and Indoor Visual Localization Challenge of ICCV 2021 Workshop on Long-term Visual Localization under Changing Conditions, as well as the Re-localization Challenge for Autonomous Driving of ICCV 2021 Workshop on Map-based Localization for Autonomous Driving.