OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB

• URL: http://arxiv.org/abs/2410.06694v1
• Date: Wed, 9 Oct 2024 09:01:40 GMT
• Title: OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB
• Authors: Yunzhi Lin, Yipu Zhao, Fu-Jen Chu, Xingyu Chen, Weiyao Wang, Hao Tang, Patricio A. Vela, Matt Feiszli, Kevin Liang,
• Abstract summary: We introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions. We present a benchmarking framework for a comprehensive comparison of pose tracking algorithms.
• Score: 40.62577054196799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions. We additionally present a benchmarking framework for a comprehensive comparison of pose tracking algorithms. We propose a pipeline featuring an uncertainty-aware keypoint refinement network, employing probabilistic modeling to refine pose estimation. Comparative evaluations demonstrate that our approach achieves performance superior to existing baselines on real datasets, underscoring the effectiveness of our synthetic dataset and refinement technique in enhancing tracking precision in dynamic contexts. Our contributions set a new precedent for the development and assessment of object pose tracking methodologies in complex scenes.

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