GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation
Author:
Funder
China Scholarship Council
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9577055/9577056/09578682.pdf?arnumber=9578682
Cited by 215 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. Addressing challenges in industrial pick and place: A deep learning-based 6 Degrees-of-Freedom pose estimation solution;Computers in Industry;2024-10
2. OA-Pose: Occlusion-aware monocular 6-DoF object pose estimation under geometry alignment for robot manipulation;Pattern Recognition;2024-10
3. A multi-feature fusion-based pose tracking method for industrial object with visual ambiguities;Advanced Engineering Informatics;2024-10
4. Six-Degree-of-Freedom Pose Estimation Method for Multi-Source Feature Points Based on Fully Convolutional Neural Network;Journal of Intelligent & Robotic Systems;2024-09-07
5. Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping;Applied Sciences;2024-09-07
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