用隐式表示实现任意尺度的立体图像超分辨率，保持左右视图几何一致性。
StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representation
- 将双目图像建模为连续隐式表示，突破固定尺度限制。
- 引入空间扭曲与交叉注意力，显著提升像素级几何一致性。
- 适用于任意尺度重建，尤其适合跨分布场景的超分辨率任务。
立体图像超分辨率（SSR）旨在通过利用双目图像对的信息增强高分辨率细节。然而，现有基于像素洗牌等上采样方法常忽略视图间几何一致性，且仅支持固定尺度上采样。核心问题在于，传统方法独立处理不同视角的深层特征，缺乏跨视角与非局部信息感知能力，难以自适应选择多视角场景中的有益信息。本文提出立体隐式神经表征（StereoINR），创新性地将双目图像对建模为连续隐式表示。该连续表示突破尺度限制，为左右视图提供任意尺度超分辨率重建的统一方案。通过引入空间扭曲与交叉注意力机制，StereoINR实现了有效的跨视角信息融合，在多个数据集上的大量实验表明，其在训练分布外尺度上采样表现优于现有方法，并在训练分布内尺度达到当前最优水平。
原文摘要 · Abstract (English)
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.
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