OpenFLAME: Federated Visual Positioning System to Enable Large-Scale Augmented Reality Applications
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914075628273664 |
|---|---|
| author | Bharadwaj, Sagar Williams, Harrison Wang, Luke Liang, Michael Jin, Tao Seshan, Srinivasan Rowe, Anthony |
| author_facet | Bharadwaj, Sagar Williams, Harrison Wang, Luke Liang, Michael Jin, Tao Seshan, Srinivasan Rowe, Anthony |
| contents | World-scale augmented reality (AR) applications need a ubiquitous 6DoF localization backend to anchor content to the real world consistently across devices. Large organizations such as Google and Niantic are 3D scanning outdoor public spaces in order to build their own Visual Positioning Systems (VPS). These centralized VPS solutions fail to meet the needs of many future AR applications -- they do not cover private indoor spaces because of privacy concerns, regulations, and the labor bottleneck of updating and maintaining 3D scans. In this paper, we present OpenFLAME, a federated VPS backend that allows independent organizations to 3D scan and maintain a separate VPS service for their own spaces. This enables access control of indoor 3D scans, distributed maintenance of the VPS backend, and encourages larger coverage. Sharding of VPS services introduces several unique challenges -- coherency of localization results across spaces, quality control of VPS services, selection of the right VPS service for a location, and many others. We introduce the concept of federated image-based localization and provide reference solutions for managing and merging data across maps without sharing private data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OpenFLAME: Federated Visual Positioning System to Enable Large-Scale Augmented Reality Applications Bharadwaj, Sagar Williams, Harrison Wang, Luke Liang, Michael Jin, Tao Seshan, Srinivasan Rowe, Anthony Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Robotics World-scale augmented reality (AR) applications need a ubiquitous 6DoF localization backend to anchor content to the real world consistently across devices. Large organizations such as Google and Niantic are 3D scanning outdoor public spaces in order to build their own Visual Positioning Systems (VPS). These centralized VPS solutions fail to meet the needs of many future AR applications -- they do not cover private indoor spaces because of privacy concerns, regulations, and the labor bottleneck of updating and maintaining 3D scans. In this paper, we present OpenFLAME, a federated VPS backend that allows independent organizations to 3D scan and maintain a separate VPS service for their own spaces. This enables access control of indoor 3D scans, distributed maintenance of the VPS backend, and encourages larger coverage. Sharding of VPS services introduces several unique challenges -- coherency of localization results across spaces, quality control of VPS services, selection of the right VPS service for a location, and many others. We introduce the concept of federated image-based localization and provide reference solutions for managing and merging data across maps without sharing private data. |
| title | OpenFLAME: Federated Visual Positioning System to Enable Large-Scale Augmented Reality Applications |
| topic | Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Robotics |
| url | https://arxiv.org/abs/2510.03915 |