Advancing 6DoF Object Pose Estimation: Keypoint Voting, Optimal Keypoint Sampling, and Bridging the Simulation-to-real Gap
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Authors
Wu, Yangzheng
Date
2025-01-30
Type
thesis
Language
eng
Keyword
6DoF pose
Alternative Title
Abstract
This thesis explores advancements in six-degree-of-freedom (6DoF) object pose estimation using point cloud and RGB-D data. Three novel methodologies are proposed to address distinct challenges in this field. First, RCVPose3D introduces a cascaded keypoint voting framework that separates semantic segmentation from keypoint regression, incorporating pairwise constraints and a Voter Confident Score to improve accuracy. RCVPose3D achieves state-of-the-art results on Occlusion LINEMOD (74.5%) and YCB-Video (96.9%), outperforming traditional RGB and RGB-D methods. Second, KeyGNet leverages a graph network to optimize the keypoint selection, improving accuracy and efficiency by learning dispersed, evenly distributed keypoints. KeyGNet enhances performance across all metrics, notably increasing ADD(S) on Occlusion LINEMOD by 16.4% and closing the single-to-multiobject training gap. Finally, RKHSPose addresses the simulation-to-real domain gap through a self-supervised framework using a learnable kernel in RKHS and an adapter network pre-trained on synthetic data. This approach achieves competitive results against fully supervised methods, requiring no real groundtruth annotations. Together, these contributions advance 6DoF pose estimation in accuracy, efficiency, and adaptability across diverse datasets and scenarios.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
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Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution 4.0 International
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution 4.0 International