Stereo image super-resolution (SSR) aims to enhance high-resolution details by leveraging information from stereo image pairs. However, existing stereo super-resolution (SSR) upsampling methods (e.g., pixel shuffle) often overlook cross-view geometric consistency and are limited to fixed-scale upsampling. The key issue is that previous upsampling methods use convolutions to independently process deep features of different views, lacking cross-view and non-local information perception, making it difficult to select beneficial information from multi-view scenes adaptively. In this work, we propose Stereo Implicit Neural Representation (StereoINR), which innovatively models stereo image pairs as continuous implicit representations. This continuous representation breaks through the scale limitations, providing a unified solution for arbitrary-scale stereo super-resolution reconstruction of left-right views. Furthermore, by incorporating spatial warping and cross-attention mechanisms, StereoINR enables effective cross-view information fusion and achieves significant improvements in pixel-level geometric consistency. Extensive experiments on multiple datasets demonstrate that StereoINR outperforms out-of-training-distribution scale upsampling and matches state-of-the-art SSR methods within training-distribution scales.

Cross-View Geo-Localization task is aimed at establishing correspondences between images captured from different perspectives within the same geographical region. The major challenge lies in the significant appearance variations of the same scene in different views. Current methods predominantly rely on learning a representation of the coarse-grained information from images and then evaluating the similarity, while the fine-grained features are usually not well-treated. In this paper, a novel method, named DAC (Domain Alignment and scene Consistency) is proposed, which leverages contrastive learning to acquire the global information of images and simultaneously employs a domain space alignment module to align the fine-grained features. The comprehensive utilization of multi-grained vision information guarantees better feature representations. Additionally, a cross-batch scene consistency strategy is proposed in the network to establish the global supervision of the positive samples based on scene correspondence, which improves the distinctiveness of the image representations. Advanced performance is shown by our method in drone-view target localization and drone navigation applications, outperforming state-of-the-art methods in comprehensive tests on the popular public datasets University-1652 and SUES-200. Our method also outperforms existing methods in cross-region localization, showing an average improvement of 5.6% in the R@1. Our codes and models are available athttps://github.com/SummerpanKing/DAC.