SURE: Semi-dense Uncertainty-REfined Feature Matching

• URL: http://arxiv.org/abs/2603.04869v1
• Date: Thu, 05 Mar 2026 06:53:11 GMT
• Title: SURE: Semi-dense Uncertainty-REfined Feature Matching
• Authors: Sicheng Li, Zaiwang Gu, Jie Zhang, Qing Guo, Xudong Jiang, Jun Cheng,
• Abstract summary: SURE is a Semi- dense Uncertainty-REfined matching framework that jointly predicts correspondences and their confidence.<n>Our approach in- troduces a novel evidential head for trustworthy coordinate regression, along with a lightweight spatial fusion module.<n>Our method consistently outperforms existing state-of-the-art semi-dense matching models in both accuracy and efficiency.
• Score: 28.68008638977835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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