用频域相位信息提升立体匹配精度，实现实时高精度效果
PhasorNet: Learning Structure from Frequency for Real-Time Stereo Matching
- 引入傅里叶相位信息增强注意力机制，强化结构一致性
- 在ETH3D上达顶尖性能，参数仅5.3M，KITT上跨域泛化优异
- 适合需要实时、高精度立体匹配的自动驾驶与机器人场景
准确的立体匹配在细结构、反光或透明物体等病态区域仍具挑战性，因外观线索常模糊或不可靠。为此，我们提出PhasorNet，一种轻量但强大的框架，通过频域线索提升几何判别力。核心是相位增强变压器（PAT），将傅里叶导出的相位信息注入注意力机制，生成对光照鲁棒、保留结构的特征，优先关注困难区域的结构一致性。此外，我们设计了几何上下文融合精炼模块（GCFRM），结合全分辨率卷积流与轻量注意力流（使用WQA和CDGA模块），高效保持细节与边界，开销小。训练通过多尺度边缘引导高误差区域（EHR）损失增强，自适应聚焦于高误差与边缘区域，指导层级成本体精炼。仅530万参数，PhasorNet在挑战性ETH3D基准上达到领先性能，并在KITTI上展现优秀跨域泛化能力，为高精度实时立体匹配提供高效实用方案。
原文摘要 · Abstract (English)
Accurate stereo matching remains challenging in ill-posed regions such as fine structures, reflective, or transparent objects, where appearance cues are often ambiguous or unreliable. To tackle this, we propose PhasorNet, a lightweight yet powerful framework that boosts geometric discrimination via frequency-domain cues. At its core, the Phase-Augmented Transformer (PAT) injects Fourier-derived phase information into the attention mechanism, yielding photometrically robust, structure-preserving features that prioritize structural consistency in difficult areas. Additionally, we develop a Geometry-Context Fusion Refinement Module (GCFRM) that combines a full-resolution convolutional stream with a lightweight attention-based stream (leveraging WQA and CDGA blocks) to efficiently preserve fine details and object boundaries without excessive overhead. Training is further enhanced by a multi-scale Edge-guided High-Error Region (EHR) loss that adaptively focuses optimization on high-error and edge regions, guiding hierarchical cost volume refinement. With only 5.3M parameters, PhasorNet achieves state-of-the-art performance on the challenging ETH3D benchmark while exhibiting excellent cross-domain generalization on KITTI, delivering an efficient and practical solution for accurate real-time stereo matching.
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