Object 6D Pose Estimation with Non-local Attention

• URL: http://arxiv.org/abs/2002.08749v1
• Date: Thu, 20 Feb 2020 14:23:32 GMT
• Title: Object 6D Pose Estimation with Non-local Attention
• Authors: Jianhan Mei, Henghui Ding, Xudong Jiang
• Abstract summary: We propose a network that integrate 6D object pose parameter estimation into the object detection framework. The proposed method reaches the state-of-the-art performance on the YCB-video and the Linemod datasets.
• Score: 29.929911622127502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the challenging task of estimating 6D object pose from a single RGB image. Motivated by the deep learning based object detection methods, we propose a concise and efficient network that integrate 6D object pose parameter estimation into the object detection framework. Furthermore, for more robust estimation to occlusion, a non-local self-attention module is introduced. The experimental results show that the proposed method reaches the state-of-the-art performance on the YCB-video and the Linemod datasets.

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