EdgePose: An Edge Attention Network for 6D Pose Estimation

Qi Feng, Jian Nong and Yanyan Liang ()
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Qi Feng: School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China
Jian Nong: School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China
Yanyan Liang: School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China

Mathematics, 2024, vol. 12, issue 17, 1-13

Abstract: We propose a 6D pose estimation method that introduces an edge attention mechanism into the bidirectional feature fusion network. Our method constructs an end-to-end network model by sharing weights between the edge detection encoder and the encoder of the RGB branch in the feature fusion network, effectively utilizing edge information and improving the accuracy and robustness of 6D pose estimation. Experimental results show that this method achieves an accuracy of nearly 100% on the LineMOD dataset, and it also achieves state-of-the-art performance on the YCB-V dataset, especially on objects with significant edge information.

Keywords: 6D pose estimation; edge attention; feature fusion; deep learning; mixed reality (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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