English(EN) PhasorNet: Learning Structure from Frequency for Real-Time Stereo Matching

PhasorNet 使用频域进行实时立体匹配

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了 PhasorNet，一个用于实时立体匹配的新框架，该框架利用频域线索来提高在挑战性场景下的准确性。该系统包含一个相位增强Transformer (PAT)，将傅里叶衍生的相位信息整合到其注意力机制中，增强了其保持结构一致性的能力。此外，一个几何上下文融合细化模块 (GCFRM) 结合了卷积和基于注意力的流，以有效地保持精细细节和物体边界。PhasorNet 使用多尺度边缘引导高误差区域 (EHR) 损失进行训练，以较低的参数量在 ETH3D 基准测试中取得了最先进的结果。 AI

影响这项研究可能为更强大、更高效的实时计算机视觉应用带来突破，尤其是在视觉条件具有挑战性的领域。

排序理由该条目是一篇学术论文，详细介绍了一种新的立体匹配方法。 [lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Md Raqib Khan, Santosh Kumar Vipparthi, Subrahmanyam Murala · 2026-09-01 04:00

PhasorNet：从频率学习结构以实现实时立体匹配

arXiv:2608.29819v1 Announce Type: new 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 lightwei…

本文如何被排名

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PhasorNet：从频率学习结构以实现实时立体匹配

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