Graph convolutional network for 3d object pose estimation in a point cloud

HIGHLIGHTS

• who: Tae-Won Jung et al. from the Department of Immersive Content Convergence, Kwangwoon University, Kwangwoon-ro, Nowon-gu have published the research work: Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud, in the Journal: Sensors 2022, 8166 of /2022/
• what: The authors propose a 3D point cloud object detection and pose estimation method based on a graph convolution network and the keypoint attention mechanism to aggregate the features of neighboring points. The authors design a point cloud-based graph convolutional network with a keypoint attention mechanism. In this work, the . . .
  

   

  

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