Fully Convolutional Geometric Features for Category-level Object Alignment-Reference-Cited by-同舟云学术

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Fully Convolutional Geometric Features for Category-level Object Alignment

Published:2020-10-24 Issue: Volume: Page:
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Container-title:2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Author:

Feng Qiaojun,Atanasov Nikolay

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9340668/9340635/09341550.pdf?arnumber=9341550

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. HEGN: Hierarchical Equivariant Graph Neural Network for 9DoF Point Cloud Registration;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02

3. Optimal and Robust Category-Level Perception: Object Pose and Shape Estimation From 2-D and 3-D Semantic Keypoints;IEEE Transactions on Robotics;2023-10

4. SGPCR: Spherical Gaussian Point Cloud Representation and its Application to Object Registration and Retrieval;2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2023-01

5. Accurate Instance-Level CAD Model Retrieval in a Large-Scale Database;2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2022-10-23

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