SSMB: Self-Supervised Local Feature Detection under Motion Blur
Keypoint detection under motion blur remains a significant challenge, as blur distorts local image structure and degrades the repeatability of feature localization. Existing approaches either rely on computationally expensive deblur-then-detect pipelines that may introduce restoration artifacts, or learn to regress the image positions of handcrafted keypoints extracted on sharp images, which reflects the assumptions of the handcrafted detector rather than what is truly repeatable under blur. We present SSMB, a deblur-free, self-supervised keypoint detector for motion-blurred images that requir
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- FuzzySimilar title/name (fuzzy) · 84%mudler/LocalAI →
“Fuzzy title match (0.92): “SSMB: Self-Supervised Local Feature Detection under Motion B” ≈ “mudler/LocalAI””
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Zhenjun Zhao →
“SSMB: Self-Supervised Local Feature Detection under Motion Blur”
- LinkedLinked via arxiv author · 85%Fabio Bellavia →
“SSMB: Self-Supervised Local Feature Detection under Motion Blur”
- LinkedLinked via arxiv author · 85%Wenting Wang →
“SSMB: Self-Supervised Local Feature Detection under Motion Blur”
- LinkedLinked via arxiv author · 85%Yanfan Zhu →
“SSMB: Self-Supervised Local Feature Detection under Motion Blur”
- LinkedLinked via arxiv author · 85%Jiajun Wu →
“SSMB: Self-Supervised Local Feature Detection under Motion Blur”