How to create a uniform-in-volume point cloud in numpy?

Question

This is actually a meshgrid in a point-cloud form (n, 3).

This code does it, and all is left is to vectorize, which I am struggling with.
Maybe some stride tricks + repeats are required here?

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np


dim_len = 5

x = np.linspace(0., 1., dim_len)
y = np.linspace(0., 1., dim_len)
z = np.linspace(0., 1., dim_len)

n_points = dim_len ** 3
point_cloud = np.zeros((n_points, 3))
# TODO vectorize
point_ind = 0
for i in range(dim_len):
    for j in range(dim_len):
        for k in range(dim_len):
            point_cloud[point_ind, 0] = x[i]
            point_cloud[point_ind, 1] = y[j]
            point_cloud[point_ind, 2] = z[k]
            point_ind += 1

print(point_cloud)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(point_cloud[:, 0], point_cloud[:, 1], point_cloud[:, 2],)
plt.show()

score 0 · Accepted Answer · answered May 03 '21 at 07:33

Following from here,

dim_len = 30
spacing = 2 / dim_len
point_cloud = np.mgrid[-1:1:spacing, -1:1:spacing, -1:1:spacing].reshape(3, -1).T

I am leaving this question undeleted because it is google searchable. It took me a while to find the reference for this.

How to create a uniform-in-volume point cloud in numpy?

1 Answers1