RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery

• URL: http://arxiv.org/abs/2309.10255v2
• Date: Wed, 18 Oct 2023 08:21:34 GMT
• Title: RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery
• Authors: Jiaxin Wei, Xibin Song, Weizhe Liu, Laurent Kneip, Hongdong Li and Pan Ji
• Abstract summary: We propose a novel pipeline that decouples the 6D pose and size estimation to mitigate the influence of imperfect scales on rigid transformations. Specifically, we leverage a pre-trained monocular estimator to extract local geometric information. A separate branch is designed to directly recover the metric scale of the object based on category-level statistics.
• Score: 72.13154206106259
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While showing promising results, recent RGB-D camera-based category-level object pose estimation methods have restricted applications due to the heavy reliance on depth sensors. RGB-only methods provide an alternative to this problem yet suffer from inherent scale ambiguity stemming from monocular observations. In this paper, we propose a novel pipeline that decouples the 6D pose and size estimation to mitigate the influence of imperfect scales on rigid transformations. Specifically, we leverage a pre-trained monocular estimator to extract local geometric information, mainly facilitating the search for inlier 2D-3D correspondence. Meanwhile, a separate branch is designed to directly recover the metric scale of the object based on category-level statistics. Finally, we advocate using the RANSAC-P$n$P algorithm to robustly solve for 6D object pose. Extensive experiments have been conducted on both synthetic and real datasets, demonstrating the superior performance of our method over previous state-of-the-art RGB-based approaches, especially in terms of rotation accuracy. Code: https://github.com/goldoak/DMSR.

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