DeepIM: Deep Iterative Matching for 6D Pose Estimation

@article{Li2019DeepIMDI,
  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},
  year={2019},
  volume={128},
  pages={657 - 678}
}
  • Yi Li, Gu Wang, +2 authors Dieter Fox
  • Published 2019
  • Computer Science
  • International Journal of Computer Vision
  • Estimating 6D poses of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the input image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 75 REFERENCES

    DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

    VIEW 2 EXCERPTS
    HIGHLY INFLUENTIAL

    Global Hypothesis Generation for 6D Object Pose Estimation

    Geometric Loss Functions for Camera Pose Regression with Deep Learning

    Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images

    VIEW 1 EXCERPT

    BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

    VIEW 13 EXCERPTS
    HIGHLY INFLUENTIAL

    3D Bounding Box Estimation Using Deep Learning and Geometry

    VIEW 1 EXCERPT

    Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image

    VIEW 11 EXCERPTS
    HIGHLY INFLUENTIAL