{
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    {
      "cell_type": "code",
      "execution_count": null,
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      },
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Surface Reconstruction {#surface_reconstruction_example}\n\nSurface reconstruction has a dedicated filter in PyVista and is handled\nby `pyvista.PolyDataFilters.reconstruct_surface`{.interpreted-text\nrole=\"func\"}. This tends to perform much better than\n`pyvista.DataSetFilters.delaunay_3d`{.interpreted-text role=\"func\"}.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import pyvista as pv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create a point cloud from a sphere and then reconstruct a surface from\nit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "points = pv.wrap(pv.Sphere().points)\nsurf = points.reconstruct_surface()\nsurf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot the point cloud and the reconstructed sphere.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "pl = pv.Plotter(shape=(1, 2))\npl.add_mesh(points)\npl.add_title('Point Cloud of 3D Surface')\npl.subplot(0, 1)\npl.add_mesh(surf, color=True, show_edges=True)\npl.add_title('Reconstructed Surface')\npl.show()"
      ]
    }
  ],
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