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EdgePose: An Edge Attention Network for 6D Pose Estimation

Author

Listed:
  • 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)

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.

Suggested Citation

  • Qi Feng & Jian Nong & Yanyan Liang, 2024. "EdgePose: An Edge Attention Network for 6D Pose Estimation," Mathematics, MDPI, vol. 12(17), pages 1-13, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2607-:d:1462212
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