StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representation
This addresses the problem of limited and inconsistent upsampling in stereo super-resolution for applications like 3D vision, though it is incremental as it builds on implicit neural representations.
The paper tackles stereo image super-resolution by proposing StereoINR, which models stereo pairs as continuous implicit representations to enable arbitrary-scale upsampling and improve cross-view geometric consistency, achieving performance that matches state-of-the-art methods within training scales and outperforms them on out-of-distribution scales.
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 convolution 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 across multiple datasets show that StereoINR outperforms out-of-training-distribution scale upsampling and matches state-of-the-art SSR methods within training-distribution scales.