-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Wei_2025_WACV, author = {Wei, Tong and Lindenberger, Philipp and Matas, Jir{\'\i} and Barath, Daniel}, title = {Breaking the Frame: Visual Place Recognition by Overlap Prediction}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {2322-2331} }
Breaking the Frame: Visual Place Recognition by Overlap Prediction
Abstract
Visual place recognition methods struggle with occlusion 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.
Related Material