Co-op: Correspondence-based Novel Object Pose Estimation

• URL: http://arxiv.org/abs/2503.17731v1
• Date: Sat, 22 Mar 2025 11:24:19 GMT
• Title: Co-op: Correspondence-based Novel Object Pose Estimation
• Authors: Sungphill Moon, Hyeontae Son, Dongcheol Hur, Sangwook Kim,
• Abstract summary: Co-op is a novel method for accurately and robustly estimating the 6DoF pose of objects unseen during training from a single RGB image.<n>Our method requires only the CAD model of the target object and can precisely estimate its pose without any additional fine-tuning.
• Score: 14.598853174946656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Co-op, a novel method for accurately and robustly estimating the 6DoF pose of objects unseen during training from a single RGB image. Our method requires only the CAD model of the target object and can precisely estimate its pose without any additional fine-tuning. While existing model-based methods suffer from inefficiency due to using a large number of templates, our method enables fast and accurate estimation with a small number of templates. This improvement is achieved by finding semi-dense correspondences between the input image and the pre-rendered templates. Our method achieves strong generalization performance by leveraging a hybrid representation that combines patch-level classification and offset regression. Additionally, our pose refinement model estimates probabilistic flow between the input image and the rendered image, refining the initial estimate to an accurate pose using a differentiable PnP layer. We demonstrate that our method not only estimates object poses rapidly but also outperforms existing methods by a large margin on the seven core datasets of the BOP Challenge, achieving state-of-the-art accuracy.

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