PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects
6D pose estimation remains a key challenge in robotics and computer vision, particularly in industrial environments. The deployment of currently available data-driven methods is often limited by resource-intensive data pipelines, reliance on textured 3D models, and sensitivity to geometric deviations caused by damages or assembly defects. We present PIXIE, a zero-shot framework that estimates the 6D pose of an object from an RGB image using only an untextured 3D model. Synthetic depth and normal maps are rendered from sampled reference viewpoints and matched to the query image via a pretrained
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- FuzzyOverlapping authors or contributors · 62%onyx-dot-app/onyx →
“Shared author/contributor keys: mohan”
- LinkedLinked via arxiv author · 85%Leon Jungemeyer →
“PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects”
- LinkedLinked via arxiv author · 85%Alejandro Magaña →
“PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects”
- LinkedLinked via arxiv author · 85%Gautham Mohan →
“PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects”
- LinkedLinked via arxiv author · 85%Matthias Karl →
“PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects”
- LinkedLinked via arxiv author · 85%Daniel Werdehausen →
“PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects”