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Scikit-Shapes 0.2 documentation
Scikit-Shapes 0.2 documentation
  • Installation
  • Gallery of examples
    • Data
      • PolyData conversion from and to PyVista or Vedo
      • The PolyData class: point cloud, wireframe and triangle meshes
    • Multiscaling
      • Multiscaling (triangle mesh)
      • Multiscaling and landmarks
      • Multiscaling and signal propagation
    • Registration
      • Intrinsic vs Extrinsic deformation
      • Rigid alignment in 2D
      • Rigid alignment in 3D with landmarks
      • Registration with LDDMM
      • LDDMM with normalized kernel
      • Elastic metric and multiscale strategy
    • Applications
      • Browse a sequence of shapes
      • Set landmarks
    • Custom model or loss function
      • Write a custom deformation model for PolyData.
  • API
    • skshapes
      • skshapes package
        • skshapes.applications package
        • skshapes.convolutions package
        • skshapes.data package
        • skshapes.decimation package
        • skshapes.features package
        • skshapes.input_validation package
        • skshapes.loss package
        • skshapes.morphing package
        • skshapes.multiscaling package
        • skshapes.optimization package
        • skshapes.tasks package
        • skshapes.triangle_mesh package
  • Contributing
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MultiscalingΒΆ

Representing data at various scale is a core feature of scikit-shapes: in addition to data compression, we provide tools for preserving features across scales.

Multiscaling (triangle mesh)

Multiscaling (triangle mesh)

Multiscaling and landmarks

Multiscaling and landmarks

Multiscaling and signal propagation

Multiscaling and signal propagation
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Multiscaling (triangle mesh)
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The PolyData class: point cloud, wireframe and triangle meshes
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