Graph neural network for 6D object pose estimation

Abstract

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).