SC-Net: Robust Correspondence Learning via Spatial and Cross-Channel Context
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911343548825600 |
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| author | Lin, Shuyuan Liao, Hailiang Qi, Qiang Huang, Junjie Lai, Taotao Weng, Jian |
| author_facet | Lin, Shuyuan Liao, Hailiang Qi, Qiang Huang, Junjie Lai, Taotao Weng, Jian |
| contents | Recent research has focused on using convolutional neural networks (CNNs) as the backbones in two-view correspondence learning, demonstrating significant superiority over methods based on multilayer perceptrons. However, CNN backbones that are not tailored to specific tasks may fail to effectively aggregate global context and oversmooth dense motion fields in scenes with large disparity. To address these problems, we propose a novel network named SC-Net, which effectively integrates bilateral context from both spatial and channel perspectives. Specifically, we design an adaptive focused regularization module (AFR) to enhance the model's position-awareness and robustness against spurious motion samples, thereby facilitating the generation of a more accurate motion field. We then propose a bilateral field adjustment module (BFA) to refine the motion field by simultaneously modeling long-range relationships and facilitating interaction across spatial and channel dimensions. Finally, we recover the motion vectors from the refined field using a position-aware recovery module (PAR) that ensures consistency and precision. Extensive experiments demonstrate that SC-Net outperforms state-of-the-art methods in relative pose estimation and outlier removal tasks on YFCC100M and SUN3D datasets. Source code is available at http://www.linshuyuan.com. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_23473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SC-Net: Robust Correspondence Learning via Spatial and Cross-Channel Context Lin, Shuyuan Liao, Hailiang Qi, Qiang Huang, Junjie Lai, Taotao Weng, Jian Computer Vision and Pattern Recognition Recent research has focused on using convolutional neural networks (CNNs) as the backbones in two-view correspondence learning, demonstrating significant superiority over methods based on multilayer perceptrons. However, CNN backbones that are not tailored to specific tasks may fail to effectively aggregate global context and oversmooth dense motion fields in scenes with large disparity. To address these problems, we propose a novel network named SC-Net, which effectively integrates bilateral context from both spatial and channel perspectives. Specifically, we design an adaptive focused regularization module (AFR) to enhance the model's position-awareness and robustness against spurious motion samples, thereby facilitating the generation of a more accurate motion field. We then propose a bilateral field adjustment module (BFA) to refine the motion field by simultaneously modeling long-range relationships and facilitating interaction across spatial and channel dimensions. Finally, we recover the motion vectors from the refined field using a position-aware recovery module (PAR) that ensures consistency and precision. Extensive experiments demonstrate that SC-Net outperforms state-of-the-art methods in relative pose estimation and outlier removal tasks on YFCC100M and SUN3D datasets. Source code is available at http://www.linshuyuan.com. |
| title | SC-Net: Robust Correspondence Learning via Spatial and Cross-Channel Context |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.23473 |