PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

@article{Gao2019PRSNetPR,
  title={PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models},
  author={Lin Gao and Ling-Xiao Zhang and Hsien-Yu Meng and Yihui Ren and Yu-Kun Lai and Leif Kobbelt},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.06511}
}
  • Lin Gao, Ling-Xiao Zhang, +3 authors Leif Kobbelt
  • Published 2019
  • Computer Science
  • ArXiv
  • In geometry processing, symmetry is the universal high level structural information of the 3d models and benefits many geometry processing tasks including shape segmentation, alignment, matching, completion, \textit{e.g.}. Thus it is an important problem to analyze various forms of the symmetry of 3D shapes. The planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time consuming and may not be able to identify all the symmetry planes. In… CONTINUE READING

    Citations

    Publications citing this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 61 REFERENCES

    ABC: A Big CAD Model Dataset for Geometric Deep Learning

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Accurate detection of symmetries in 3D shapes

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Thingi10K: A Dataset of 10, 000 3D-Printing Models

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Adam: A Method for Stochastic Optimization

    VIEW 2 EXCERPTS
    HIGHLY INFLUENTIAL

    SCAPE: shape completion and animation of people

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    ShapeNet: An Information-Rich 3D Model Repository

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    3D ShapeNets: A deep representation for volumetric shapes

    VIEW 1 EXCERPT