English

MegLoc: A Robust and Accurate Visual Localization Pipeline

Computer Vision and Pattern Recognition 2022-05-19 v2

Abstract

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.

Keywords

Cite

@article{arxiv.2111.13063,
  title  = {MegLoc: A Robust and Accurate Visual Localization Pipeline},
  author = {Shuxue Peng and Zihang He and Haotian Zhang and Ran Yan and Chuting Wang and Qingtian Zhu and Xiao Liu},
  journal= {arXiv preprint arXiv:2111.13063},
  year   = {2022}
}
R2 v1 2026-06-24T07:52:02.325Z