Plot 3D Alpha Shape


I am trying to implement Edelsbrunner’s Algorithm for the alpha shape of a 3D point cloud in python as presented in this SO post. However, I’m having trouble plotting results. Half my sphere looks good, and the other half garbled.

I suspect it may have something to do with the fact that I have negative coordinates, but I’m not sure.


I’m adding these so programmers can contribute without being bogged down by math. These are simplified definitions and not meant to be precise (Feel free to skip this part; for more, see see Introduction to Alpha Shapes and Discrete Differential Geometry):

  • Delaunay triangulation: for a 2D point set, a tesselation of the points into triangles (i.e. “triangulation”) where the circumscribed circle (i.e. “circumcircle”) about every triangle contains no other points in the set. For 3D points, replace “triangle” with “tetrahedron” and “circumcircle” with “circumsphere”.

  • Affinely independent: a collection of points p0, ..., pk such that all vectors vi := pi-p0 are linearly independent (i.e. in 2D not collinear, in 3D not coplanar); also called “points in general position”

  • k-simplex: the convex hull of k+1 affinely-independent points; we call the points its vertices.

0-simplex = point (consists of 0+1 = 1 points)
1-simplex = line (consists of 1+1 = 2 points)
2-simplex = triangle (consists of 2+1 = 3 points)
3-simplex = tetrahedron (consists of 3+1 = 4 points)

  • face: any simplex whose vertices are a subset of the vertices of another simplex; i.e. “a part of a simplex”

  • (geometric) simplicial complex: a collection of simplices where (1) the intersection of two simplices is a simplex, and (2) every face of a simplex is in the complex; i.e. “a bunch of simplices”

  • alpha-exposed: a simplex within a point set where the circle (2D) or ball (3D) of radius alpha through its vertices doesn’t contain any other point in the point set

  • alpha shape: the boundary of all alpha-exposed simplices of a point set


Edelsbrunner’s Algorithm is as follows:

Given a point cloud pts:

  1. Compute the Delaunay triangulation DT of the point cloud
  2. Find the alpha-complex: search all simplices in the Delaunay triangulation and (a) if any ball around a simplex is empty and has
    a radius less than alpha (called the “alpha test”), then add it to the
    alpha complex
  3. The boundary of the alpha complex is the alpha shape


from scipy.spatial import Delaunay
import numpy as np
from collections import defaultdict
from matplotlib import pyplot as plt
import pyvista as pv

fig = plt.figure()
ax = plt.axes(projection="3d")

plotter = pv.Plotter()

def alpha_shape_3D(pos, alpha):
    Compute the alpha shape (concave hull) of a set of 3D points.
        pos - np.array of shape (n,3) points.
        alpha - alpha value.
        outer surface vertex indices, edge indices, and triangle indices

    tetra = Delaunay(pos)
    # Find radius of the circumsphere.
    # By definition, radius of the sphere fitting inside the tetrahedral needs 
    # to be smaller than alpha value
    tetrapos = np.take(pos,tetra.vertices,axis=0)
    normsq = np.sum(tetrapos**2,axis=2)[:,:,None]
    ones = np.ones((tetrapos.shape[0],tetrapos.shape[1],1))
    a = np.linalg.det(np.concatenate((tetrapos,ones),axis=2))
    Dx = np.linalg.det(np.concatenate((normsq,tetrapos[:,:,[1,2]],ones),axis=2))
    Dy = -np.linalg.det(np.concatenate((normsq,tetrapos[:,:,[0,2]],ones),axis=2))
    Dz = np.linalg.det(np.concatenate((normsq,tetrapos[:,:,[0,1]],ones),axis=2))
    c = np.linalg.det(np.concatenate((normsq,tetrapos),axis=2))
    r = np.sqrt(Dx**2+Dy**2+Dz**2-4*a*c)/(2*np.abs(a))

    # Find tetrahedrals
    tetras = tetra.vertices[r<alpha,:]
    # triangles
    TriComb = np.array([(0, 1, 2), (0, 1, 3), (0, 2, 3), (1, 2, 3)])
    Triangles = tetras[:,TriComb].reshape(-1,3)
    Triangles = np.sort(Triangles,axis=1)
    # Remove triangles that occurs twice, because they are within shapes
    TrianglesDict = defaultdict(int)
    for tri in Triangles:TrianglesDict[tuple(tri)] += 1
    Triangles=np.array([tri for tri in TrianglesDict if TrianglesDict[tri] ==1])
    EdgeComb=np.array([(0, 1), (0, 2), (1, 2)])

    Vertices = np.unique(Edges)
    return Vertices,Edges,Triangles

def ptcloud_sphere():
    r = 3
    phi = np.linspace(0, np.pi, 18)
    theta = np.linspace(0, 2 * np.pi, 36)

    PHI, THETA = np.meshgrid(phi, theta)

    x = r * np.sin(PHI) * np.cos(THETA)
    y = r * np.sin(PHI) * np.sin(THETA)
    z = r * np.cos(PHI)

    ax.scatter(x, y, z)
    pts = np.stack((x.ravel(), y.ravel(), z.ravel()), axis=1)

    return np.unique(pts, axis=0)

if __name__ == "__main__":
    pts = ptcloud_sphere()

    verts, edges, faces = alpha_shape_3D(pts, alpha=10)

    faces_conn_list = np.insert(faces, 0, 3, axis=1)
    num_faces = faces.shape[0]

    mesh = pv.PolyData(pts[verts], faces_conn_list, n_faces=num_faces)

    plotter.add_mesh(mesh, reset_camera=True)


Point Cloud:

Point Cloud

Alpha Shape:

Alpha Shape

@MatthewFlamm @mwestphal @ben.boeckel Any ideas?

Update 09-SEP-2021

Per @akaszynski, the problem does indeed appear to be a combination of unique and negative points. He fixed this issue with the following:

pts = np.stack((x.ravel(), y.ravel(), z.ravel()), axis=1) + 10

return np.unique(pts, axis=0) - 10

Fixed Sphere

However, if someone could perform a deeper investigation to determine why this is the problem, that would help.

As far as this question relates to VTK, please not that vtkDelaunay3D incorporates some support for alpha shapes. Please see the documentation for more details.

As for debugging your code, you can try a couple things:

  • make all coordinates positive to rule out negative coordinates being an issue (I doubt that is an issue)
  • build a subset of the final PolyData from a subset of the faces and examine point ids to see where things are going wrong.

That is the extent of the suggestions I am able to provide.

@cory.quammen Thank you for the suggestion regarding vtkDelaunay3D. I am noticing on my system that this will take roughly 5 hours to complete for a dataset I am using that has ~12 million points.

Is there a way to make this run faster? I am using the pip install of vtk-9.0.3 on Windows 10 x64.

A quick Google search seems to indicate that parallel versions of Delaunay 3D exist. For example,

Paper 01
Paper 02

is there a parallel vtkDelaunay3D?

Alas, no.