Breaking the Frame: Visual Place Recognition by Overlap Prediction
<|reference_start|>Breaking the Frame: Visual Place Recognition by Overlap Prediction: Visual place recognition methods struggle with occlusions and partial visual overlaps. We propose a novel visual place recognition approach based on overlap prediction, called VOP, shifting from traditional reliance on global image similarities and local features to image overlap prediction. VOP proceeds co-visible image sections by obtaining patch-level embeddings using a Vision Transformer backbone and establishing patch-to-patch correspondences without requiring expensive feature detection and matching. Our approach uses a voting mechanism to assess overlap scores for potential database images. It provides a nuanced image retrieval metric in challenging scenarios. Experimental results show that VOP leads to more accurate relative pose estimation and localization results on the retrieved image pairs than state-of-the-art baselines on a number of large-scale, real-world indoor and outdoor benchmarks. The code is available at https://github.com/weitong8591/vop.git.<|reference_end|>
@article{wei2024breaking,
    title={Breaking the Frame: Visual Place Recognition by Overlap Prediction},
    author={Tong Wei, Philipp Lindenberger, Jiri Matas, Daniel Barath},
    journal={arXiv preprint arXiv:2406.16204},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16204},
    primaryClass={cs.CV}
}