DeepIM: Deep Iterative Matching for 6D Pose Estimation

  title={DeepIM: Deep Iterative Matching for 6D Pose Estimation},
  author={Yi Li and Gu Wang and Xiangyang Ji and Yu Xiang and Dieter Fox},
  journal={International Journal of Computer Vision},
  • Yi LiGu Wang D. Fox
  • Published 31 March 2018
  • Computer Science
  • International Journal of Computer Vision
Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality. [] Key Method Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using an untangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks…

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