Semi-Dense Matching Uncertainty Is Not Just Local Confidence

• URL: http://arxiv.org/abs/2608.08685v1
• Date: Sun, 09 Aug 2026 13:01:20 GMT
• Title: Semi-Dense Matching Uncertainty Is Not Just Local Confidence
• Abstract summary: We propose a lightweight, post-hoc overall uncertainty estimation framework that introduces a two-component Laplace mixture model with only 9 learnable parameters.<n>The objective is to explicitly capture both the sharp local refinement noise and the broader tail of coarse-assignment failures.<n>Our method consistently improves downstream geometric accuracy across various pretrained-only matchers and robust estimators with minimal computational overhead.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable semi-dense matching is essential for modern geometric vision systems. Designed under a coarse-to-fine paradigm, it achieves an optimal balance between performance and computational cost. However, existing methods often struggle to provide well-quantified uncertainties, where catastrophic coarse-assignment failures are ignored, leading to truncated error distributions and severely misjudged geometric estimations. In this paper, we propose a lightweight, post-hoc overall uncertainty estimation framework that introduces a two-component calibrated Laplace mixture model with only 9 learnable parameters. The objective is to explicitly capture both the sharp local refinement noise and the broader tail of coarse-assignment failures. We introduce the Coarse-success posterior Refit (CoRe) method, a geometric refitting module that utilizes the posterior probability of coarse-assignment success as soft correspondence weights. Extensive experiments show that our method consistently improves downstream geometric accuracy across various pretrained-only matchers and robust estimators with minimal computational overhead. Our code is available at https://github.com/khoavpt/Probabilistic-matching.

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