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 we

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ethics.ai (15 July 2026), “SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment,” evidence record 11339, https://ethics.ai/record/11339 (originally published by HuggingFace Daily Papers).

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