用2D卷积实现高效实时立体匹配，精度超越现有方法。
MAFNet:Multi-frequency Adaptive Fusion Network for Real-time Stereo Matching
- 分频域自适应融合：将代价体分解为高低频，分别聚合特征。
- 引入低秩注意力机制，融合高频与低频信息，提升估计鲁棒性。
- 仅用2D卷积，适合移动端部署，兼顾精度与实时性。
现有立体匹配网络通常依赖基于3D卷积的代价体构建或基于迭代优化的变形方法。前者在代价聚合阶段计算开销大，后者难以建模非局部上下文信息。这些方法在资源受限的移动设备上兼容性差，限制了其在实时应用中的部署。为此，我们提出多频自适应融合网络（MAFNet），仅使用高效的2D卷积即可生成高质量视差图。具体地，设计自适应频域滤波注意力模块，将完整代价体分解为高低频子体，分别进行频域感知特征聚合。随后，引入基于Linformer的低秩注意力机制，自适应融合高低频信息，实现更鲁棒的视差估计。大量实验表明，MAFNet在Scene Flow和KITTI 2015等公开数据集上显著优于现有实时方法，展现出精度与实时性能的良好平衡。
原文摘要 · Abstract (English)
Existing stereo matching networks typically rely on either cost-volume construction based on 3D convolutions or deformation methods based on iterative optimization. The former incurs significant computational overhead during cost aggregation, whereas the latter often lacks the ability to model non-local contextual information. These methods exhibit poor compatibility on resource-constrained mobile devices, limiting their deployment in real-time applications. To address this, we propose a Multi-frequency Adaptive Fusion Network (MAFNet), which can produce high-quality disparity maps using only efficient 2D convolutions. Specifically, we design an adaptive frequency-domain filtering attention module that decomposes the full cost volume into high-frequency and low-frequency volumes, performing frequency-aware feature aggregation separately. Subsequently, we introduce a Linformer-based low-rank attention mechanism to adaptively fuse high- and low-frequency information, yielding more robust disparity estimation. Extensive experiments demonstrate that the proposed MAFNet significantly outperforms existing real-time methods on public datasets such as Scene Flow and KITTI 2015, showing a favorable balance between accuracy and real-time performance.
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