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About this paper
Anastasiia Kornilova, Ivan Moskalenko, Timofei Pushkin, Fakhriddin Tojiboev, Rahim Tariverdizadeh, Gonzalo Ferrer
The Skolkovo Institute of Science and Technology research introduces an automated pipeline for creating compact Visual Place Recognition databases for indoor environments. Their approach, leveraging a dominating set algorithm on a graph of spatial image overlaps, reduces database size by up to 960 times while improving localization quality to 99.2% Recall@1 on datasets like 7-Scenes through weakly-supervised fine-tuning.
Key insights
- Framing VPR database selection as a minimum dominating set problem ensures comprehensive environment coverage with minimal database redundancy.
- Using spatial information (IoU of observed voxels) for graph construction effectively mitigates visual aliasing in complex indoor environments, which is common in VPR.
- Automated weakly-supervised labeling significantly boosts the performance of state-of-the-art VPR models in new target environments, validating the strategy of domain adaptation for improved accuracy.
Summary by alphaXiv.
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