Object 6D pose estimation with non-local attention

Jianhan Mei; Henghui Ding; Xudong Jiang

doi:10.1117/12.2573051

12 June 2020 Object 6D pose estimation with non-local attention

Jianhan Mei, Henghui Ding, Xudong Jiang

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115191H (2020) https://doi.org/10.1117/12.2573051
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

In this paper, we address the challenging task of estimating 6D object poses from a single RGB image. Motivated by the deep learning-based object detection methods, we propose a concise and efficient network that integrates 6D object pose parameter estimation into the object detection framework. Furthermore, for more robust estimation to occlusion, a nonlocal self-attention module is introduced. The experimental results show that the proposed method reaches the state-ofthe-art performance on the YCB-video and the Linemod datasets.

Citation Download Citation

Jianhan Mei, Henghui Ding, and Xudong Jiang "Object 6D pose estimation with non-local attention", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115191H (12 June 2020); https://doi.org/10.1117/12.2573051

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KEYWORDS

Cameras

RGB color model

3D modeling

3D image processing

Neural networks

Feature extraction

3D acquisition

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