# Hybrid Structure-from-Motion and Camera Relocalization for Enhanced Egocentric Localization

arXiv: https://arxiv.org/abs/2407.08023 · HTML: https://mlsanity.com/paper/2407.08023/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 9 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://mlsanity.com/paper/1711.09869/) (2017)
- [EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual Queries](https://mlsanity.com/paper/2212.06969/) (2022)
- [Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization](https://mlsanity.com/paper/2211.10528/) (2022)
- [Estimating more camera poses for ego-centric videos is essential for VQ3D](https://mlsanity.com/paper/2211.10284/) (2022)
- [EgoCOL: Egocentric Camera pose estimation for Open-world 3D object Localization @Ego4D challenge 2023](https://mlsanity.com/paper/2306.16606/) (2023)

## Built on by (most cited first, 1 of 1)

- [EAGLE: Episodic Appearance- and Geometry-aware Memory for Unified 2D-3D Visual Query Localization in Egocentric Vision](https://mlsanity.com/paper/2511.08007/) (2025)

## Abstract (arXiv)

We built our pipeline EgoLoc-v1, mainly inspired by EgoLoc. We propose a model ensemble strategy to improve the camera pose estimation part of the VQ3D task, which has been proven to be essential in previous work. The core idea is not only to do SfM for egocentric videos but also to do 2D-3D matching between existing 3D scans and 2D video frames. In this way, we have a hybrid SfM and camera relocalization pipeline, which can provide us with more camera poses, leading to higher QwP and overall success rate. Our method achieves the best performance regarding the most important metric, the overall success rate. We surpass previous state-of-the-art, the competitive EgoLoc, by 1.5%. The code is available at urlhttps://github.com/Wayne-Mai/egolocv1.