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2024
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Titel
Extending 6D Object Pose Estimators for Stereo Vision
Titel Supplements
Published on arXiv
Abstract
Estimating the 6D pose of objects accurately, quickly, and robustly remains a difficult task. However, recent methods for directly regressing poses from RGB images using dense features have achieved state-of-the-art results. Stereo vision, which provides an additional perspective on the object, can help reduce pose ambiguity and occlusion. Moreover, stereo can directly infer the distance of an object, while mono-vision requires internalized knowledge of the object's size. To extend the state-of-the-art in 6D object pose estimation to stereo, we created a BOP compatible stereo version of the YCB-V dataset. Our method outperforms state-of-the-art 6D pose estimation algorithms by utilizing stereo vision and can easily be adopted for other dense feature-based algorithms.
Author(s)
Tags
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Branche: Automotive Industry
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Branche: Healthcare
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Branche: Bioeconomics
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Branche: Cultural und Creative Economy
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Research Line: Computer graphics (CG)
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Research Line: Computer vision (CV)
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Research Line: Human computer interaction (HCI)
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Research Line: Machine learning (ML)
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LTA: Monitoring and control of processes and systems
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LTA: Machine intelligence, algorithms, and data structures (incl. semantics)
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Computer vision
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Machine learning
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Robot vision
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Object pose estimation