ABC: A Big CAD Model Dataset for Geometric Deep Learning

@article{Koch2019ABCAB,
  title={ABC: A Big CAD Model Dataset for Geometric Deep Learning},
  author={Sebastian Koch and Albert Matveev and Zhongshi Jiang and Francis Williams and Alexey Artemov and Evgeny Burnaev and Marc Alexa and Denis Zorin and Daniele Panozzo},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={9593-9603}
}
  • Sebastian Koch, Albert Matveev, +6 authors Daniele Panozzo
  • Published 2019
  • Computer Science
  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 21 CITATIONS

    ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

    VIEW 2 EXCERPTS
    CITES METHODS

    Deep Vectorization of Technical Drawings

    VIEW 1 EXCERPT
    CITES METHODS

    IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

    VIEW 1 EXCERPT

    X-CAD

    VIEW 1 EXCERPT
    CITES METHODS

    FILTER CITATIONS BY YEAR

    2019
    2020

    CITATION STATISTICS

    • 3 Highly Influenced Citations

    • Averaged 11 Citations per year from 2019 through 2020

    • 33% Increase in citations per year in 2020 over 2019

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 58 REFERENCES

    PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

    VIEW 1 EXCERPT

    3D ShapeNets: A deep representation for volumetric shapes

    VIEW 2 EXCERPTS

    PCPNET: Learning Local Shape Properties from Raw Point Clouds

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis

    VIEW 1 EXCERPT

    Dense Human Body Correspondences Using Convolutional Networks

    VIEW 1 EXCERPT