Convex Hull with vtk, pyvista, and scipy

gistfile1.py

import numpy as np
from scipy.spatial import ConvexHull
from pyvista import PolyData

def polyhull(x, y, z):
    hull = ConvexHull(np.column_stack((x, y, z)))
    faces = np.column_stack((3*np.ones((len(hull.simplices), 1), dtype=np.int), hull.simplices)).flatten()
    poly = PolyData(hull.points, faces)
    return poly
    
x, y, z = np.random.rand(100, 3).T

hull = polyhull(x, y, z)
hull.plot()

I've found scipy's ConvexHull to be 500X faster than using vtkDelaunay3D and vtkDataSetSurfaceFilter because you skip the expensive 3D tesselation of the volume.

Ok, so it looks like Delaunay scales at log(n) with the number of points while Convex Hull doesn't. Above 1000 points, the Delaunay method becomes prohibitively slow.

Without logging the y-axis the difference is stark. Delaunay starts taking seconds near 10,000 where scipy's ConvexHull stays in millisecond territory.

Logging the y-axis doesn't show the contrast as starkly, but allows the data to be read more clearly.