PhasorNet: Learning Structure from Frequency for Real-Time Stereo Matching

• URL: http://arxiv.org/abs/2608.29819v1
• Date: Sun, 30 Aug 2026 14:41:36 GMT
• Title: PhasorNet: Learning Structure from Frequency for Real-Time Stereo Matching
• Abstract summary: PhasorNet is a powerful framework that boosts geometric discrimination via frequency-domain cues.<n>With only 5.3M parameters, PhasorNet achieves state-of-the-art performance on the challenging ETH3D benchmark.
• Score: 21.209111801863546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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