Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition

• URL: http://arxiv.org/abs/2510.17739v1
• Date: Mon, 20 Oct 2025 16:50:03 GMT
• Title: Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition
• Authors: Timur Ismagilov, Shakaiba Majeed, Michael Milford, Tan Viet Tuyen Nguyen, Sarvapali D. Ramchurn, Shoaib Ehsan,
• Abstract summary: We address multi-reference visual place recognition (VPR), where reference sets captured under varying conditions are used to improve localisation performance.<n>We propose a training-free, agnostic approach that jointly models places using multiple reference descriptors via matrix decomposition into basis representations.<n>On multi-appearance data, our method improves Recall@1 by up to 18% over single-reference and outperforms multi-reference baselines across appearance and viewpoint changes.
• Score: 14.020214078011515
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address multi-reference visual place recognition (VPR), where reference sets captured under varying conditions are used to improve localisation performance. While deep learning with large-scale training improves robustness, increasing data diversity and model complexity incur extensive computational cost during training and deployment. Descriptor-level fusion via voting or aggregation avoids training, but often targets multi-sensor setups or relies on heuristics with limited gains under appearance and viewpoint change. We propose a training-free, descriptor-agnostic approach that jointly models places using multiple reference descriptors via matrix decomposition into basis representations, enabling projection-based residual matching. We also introduce SotonMV, a structured benchmark for multi-viewpoint VPR. On multi-appearance data, our method improves Recall@1 by up to ~18% over single-reference and outperforms multi-reference baselines across appearance and viewpoint changes, with gains of ~5% on unstructured data, demonstrating strong generalisation while remaining lightweight.

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