SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment
CAD-to-image alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, enabling applications in robotics and augmented reality. Recent zero-shot methods use visual foundation models to match image regions to CAD models, yet typically their correspondences are appearance-driven and degrade under occlusion or sim-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD Alignment), a weakly-supervised framework for zero-shot CAD alignment with two key contributions. First, SUFLECA sca
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignm” ≈ “Tongyi-MAI/Z-Image-Turbo””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignm” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Saad Ejaz →
“SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment”
- LinkedLinked via arxiv author · 85%Miguel Fernandez-Cortizas →
“SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment”
- LinkedLinked via arxiv author · 85%Javier Civera →
“SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment”
- LinkedLinked via arxiv author · 85%Holger Voos →
“SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment”
- LinkedLinked via arxiv author · 85%Jose Luis Sanchez-Lopez →
“SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment”