Breaking the Frame: Visual Place Recognition by Overlap Prediction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916506678329344 |
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| author | Wei, Tong Lindenberger, Philipp Matas, Jiri Barath, Daniel |
| author_facet | Wei, Tong Lindenberger, Philipp Matas, Jiri Barath, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16204 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Breaking the Frame: Visual Place Recognition by Overlap Prediction Wei, Tong Lindenberger, Philipp Matas, Jiri Barath, Daniel Computer Vision and Pattern Recognition 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. |
| title | Breaking the Frame: Visual Place Recognition by Overlap Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.16204 |