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Graph neural network for 6D object pose estimation
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DOI：10.1016/j.knosys.2021.106839.png)
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Abstract
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6D object pose estimation plays an important role in various applications such as robot manipulation and virtual reality. In this paper, we introduce a graph convolution neural network based method to addresses the problem of estimating the 6D pose of objects from a single RGB-D image. The proposed method fuses the appearance feature of the RGB image with the geometry feature of point clouds to predict pixel-level pose and the network also predicts pixel-level confidences to prune outlier predictions. The inner structure information of point cloud is learned by a graph convolution neural network. Specially, we adopt a residual graph convolution module to learn a discriminative feature. Our network enables end-to-end training and fast inference. The extensive experiments verify the method and the model achieves state-of-the-art for the LINEMOD and LINEMOD-OCCLUSION dataset (ADD-S: 88.68 and 65.38 respectively). (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Pose estimation
Image processing
Deep learning
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