PartNet: A Recursive Part Decomposition Network for Fine-Grained and Hierarchical Shape Segmentation

@article{Yu2019PartNetAR,
  title={PartNet: A Recursive Part Decomposition Network for Fine-Grained and Hierarchical Shape Segmentation},
  author={Fenggen Yu and Kun Liu and Yan Zhang and Chenyang Zhu and Kai Xu},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={9483-9492}
}
  • Fenggen Yu, Kun Liu, +2 authors Kai Xu
  • Published 2019
  • Computer Science
  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. These models are trained for a fixed set of labels, which greatly limits their flexibility and adaptivity. We opt for top-down recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on recursive neural networks. Starting from a full shape represented as a point cloud, our model performs recursive binary decomposition, where… CONTINUE READING

    Citations

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    SHOWING 1-10 OF 13 CITATIONS

    PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding

    • Kaichun Mo, Shilin Zhu, +4 authors Hao Su
    • Computer Science
    • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2019
    VIEW 1 EXCERPT
    CITES BACKGROUND

    StructureNet: Hierarchical Graph Networks for 3D Shape Generation

    VIEW 1 EXCERPT
    CITES METHODS

    Learning adaptive hierarchical cuboid abstractions of 3D shape collections

    VIEW 1 EXCERPT
    CITES METHODS

    Learning to Segment 3D Point Clouds in 2D Image Space

    VIEW 16 EXCERPTS
    CITES BACKGROUND
    HIGHLY INFLUENCED

    CvxNets: Learnable Convex Decomposition

    VIEW 1 EXCERPT

    PIE-NET: Parametric Inference of Point Cloud Edges

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    CITES BACKGROUND

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 48 REFERENCES

    PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding

    • Kaichun Mo, Shilin Zhu, +4 authors Hao Su
    • Computer Science
    • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2019

    Learning to group and label fine-grained shape components

    VIEW 1 EXCERPT

    3D Shape Segmentation with Projective Convolutional Networks

    VIEW 1 EXCERPT

    GRASS: generative recursive autoencoders for shape structures

    VIEW 2 EXCERPTS

    3D shape segmentation via shape fully convolutional networks

    VIEW 1 EXCERPT

    Im2Struct: Recovering 3D Shape Structure from a Single RGB Image

    VIEW 1 EXCERPT