FGCT6D: Frequency-Guided CNN-Transformer Fusion Network for Metal Parts' Robust 6D Pose Estimation
Author:
Affiliation:
1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/7083369/10474437/10478121.pdf?arnumber=10478121
Reference37 articles.
1. A Sim-to-Real Object Recognition and Localization Framework for Industrial Robotic Bin Picking
2. Pseudo-Siamese Graph Matching Network for Textureless Objects’ 6-D Pose Estimation
3. Learning to Assemble: Estimating 6D Poses for Robotic Object-Object Manipulation
4. BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth
5. Real-Time Seamless Single Shot 6D Object Pose Prediction
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