TextFM：语言引导的鲁棒半稠密特征匹配
TextFM: Robust Semi-dense Feature Matching with Language Guidance
将视觉语言模型（VLM）的文本嵌入作为实例级查询引入无检测器特征匹配的粗匹配阶段，为视觉特征提供域不变的语义上下文；同时用LoRA微调视觉基础模型并融合光照不变物理先验，提升跨域和极端光照下的匹配鲁棒性。
原文依据 TextFM leverages textual embeddings as instance-level queries to provide global semantic context during coarse-level matching
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论特征匹配是几何感知中的关键任务，然而现有方法往往因依赖纯视觉学习和昂贵的3D监督而在域偏移和光照变化下表现不佳。本文提出TextFM，首个语言引导的特征匹配框架，融合了视觉语言模型（VLM）提供的域不变语义信息。TextFM基于无检测器架构，利用文本嵌入作为实例级查询，在粗匹配阶段提供全局语义上下文，从而增强在无纹理表面和跨域偏移等挑战性场景下的鲁棒性。此外，我们整合了光照不变的物理先验，并应用低秩适配（LoRA）高效微调视觉基础模型（VFM），以实现更鲁棒的视觉特征提取。在室外和室内数据集上的大量实验表明，我们的方法优于其他最优方法。此外，我们贡献了一个合成昼夜匹配基准，用于极端光照条件下的严格评估。我们的方法和数据集共同为现实约束下鲁棒且可泛化的特征匹配奠定了坚实基础。
英文原文摘要 1245 字符
Feature matching is a critical task in geometric perception, yet existing methods often struggle under domain shifts and illumination changes due to reliance on visual-only learning and expensive 3D supervision. In this paper, we present TextFM, the first language-guided feature matching framework that incorporates domain-invariant semantic information from vision-language models (VLMs). Built upon a detector-free architecture, TextFM leverages textual embeddings as instance-level queries to provide global semantic context during coarse-level matching, enhancing robustness in challenging scenarios such as textureless surfaces and cross-domain shifts. Additionally, we integrate illumination-invariant physical priors and apply Low-Rank Adaptation (LoRA) to efficiently fine-tune Vision Foundation Models (VFMs) for more robust visual feature extraction. Extensive experiments on outdoor and indoor datasets show that our method outperforms other state-of-the-art methods. In addition, we contribute a synthetic day-night matching benchmark for rigorous evaluation under extreme lighting conditions. Together, our method and dataset establish a strong foundation for robust and generalizable feature matching under real-world constraints.