Cross-Camera View-Overlap Recognition
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Abstract (arXiv)
We propose a decentralised view-overlap recognition framework that operates across freely moving cameras without the need of a reference 3D map. Each camera independently extracts, aggregates into a hierarchical structure, and shares feature-point descriptors over time. A view overlap is recognised by view-matching and geometric validation to discard wrongly matched views. The proposed framework is generic and can be used with different descriptors. We conduct the experiments on publicly available sequences as well as new sequences we collected with hand-held cameras. We show that Oriented FAST and Rotated BRIEF (ORB) features with Bags of Binary Words within the proposed framework lead to higher precision and a higher or similar accuracy compared to NetVLAD, RootSIFT, and SuperGlue.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 42 references that are in the map. First posted on arXiv 24 Aug 2022.
Builds on
12 of 42 references.
References · 12
- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,443
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,320
- 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
- 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 941
- 2018From Coarse to Fine: Robust Hierarchical Localization at Large Scalecited by 559
- 2019D2-Net: A Trainable CNN for Joint Detection and Description of Local Featurescited by 326
- 2019MAGSAC++, a fast, reliable and accurate robust estimatorcited by 161
- 2018LDSO: Direct Sparse Odometry with Loop Closurecited by 51
- 2018Hybrid Scene Compression for Visual Localizationcited by 40
- 2017Data-Efficient Decentralized Visual SLAMcited by 23
- 2018iBoW-LCD: An Appearance-based Loop Closure Detection Approach using Incremental Bags of Binary Wordscited by 20
- 2018HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognitioncited by 4
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