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Object 6D Pose Estimation with Non-local Attention

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arxiv 2002.08749 v1 pith:GCX5NQ6W submitted 2020-02-20 cs.CV cs.LGeess.IV

Jianhan Mei , Henghui Ding , Xudong Jiang This is my paper · ORCID
classification cs.CVcs.LGeess.IV
keywords objectestimationposedetectionnon-localaddressattentionchallenging
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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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