Extending 6D Object Pose Estimators for Stereo Vision

• URL: http://arxiv.org/abs/2402.05610v2
• Date: Tue, 10 Sep 2024 13:51:00 GMT
• Title: Extending 6D Object Pose Estimators for Stereo Vision
• Authors: Thomas Pöllabauer, Jan Emrich, Volker Knauthe, Arjan Kuijper,
• Abstract summary: We create a BOP compatible stereo version of the YCB-V dataset for 6D object pose estimation. 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.
• Score: 4.818865062632567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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