通过不确定性建模提升图像匹配的可靠性，尤其在视角变化大时表现更优。
SURE: Semi-dense Uncertainty-REfined Feature Matching
- 联合预测匹配点与置信度，引入两类不确定性建模。
- 在多个标准数据集上精度和效率均优于现有最佳方法。
- 适合对匹配可靠性要求高的机器人视觉任务。
建立可靠的图像对应关系对众多机器人视觉问题至关重要。然而，现有方法在视角变化大或纹理缺失区域仍易产生错误匹配，且错误匹配可能获得高相似度评分。这主要源于传统模型仅依赖特征相似性，缺乏对预测匹配可靠性的显式估计，导致过度自信的错误。为此，我们提出 SURE：一种半稠密不确定性精炼匹配框架，通过同时建模随机不确定性与认知不确定性，联合预测匹配及其置信度。该方法引入新型证据头实现可信坐标回归，并设计轻量级空间融合模块，在几乎无额外开销下提升局部特征精度。我们在多个标准基准上评估了该方法，结果表明其在准确性和效率上持续优于现有的最先进半稠密匹配模型。
原文摘要 · Abstract (English)
Establishing reliable image correspondences is essential for many robotic vision problems. However, existing methods often struggle in challenging scenarios with large viewpoint changes or textureless regions, where incorrect cor- respondences may still receive high similarity scores. This is mainly because conventional models rely solely on fea- ture similarity, lacking an explicit mechanism to estimate the reliability of predicted matches, leading to overconfident errors. To address this issue, we propose SURE, a Semi- dense Uncertainty-REfined matching framework that jointly predicts correspondences and their confidence by modeling both aleatoric and epistemic uncertainties. Our approach in- troduces a novel evidential head for trustworthy coordinate regression, along with a lightweight spatial fusion module that enhances local feature precision with minimal overhead. We evaluated our method on multiple standard benchmarks, where it consistently outperforms existing state-of-the-art semi-dense matching models in both accuracy and efficiency. our code will be available on https://github.com/LSC-ALAN/SURE.
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