NumPy 3d translation matrix

Here's how to translate 3d points in Python using a translation matrix.

To translate a series of points in three dimensions in Cartesian space (x, y, z) you first need to "homogenize" the points by adding a value to their projective dimension—which we'll set to one to maintain the point's original coordinates, and then multiply our point cloud using NumPy's np.matmul method by a transformation matrix constructed from a (4, 4) identity matrix with three translation parameters in its bottom row (tx, ty, tz).

Steps

Here's a breakdown of the steps.

• Import the NumPy Python library
• Define a point cloud with Cartesian coordinates (x, y, z)
• Convert the points to homogeneous coordinates (x, y, z, w)
• Define our translation parameters (tx, ty, tz)
• Construct the translation matrix
• Multiply the homogenized point cloud by the transformation matrix with NumPy's np.matmul

Code

# translate.py
import numpy as np

# Define a set of Cartesian (x, y, z) points
point_cloud = [
    [0, 0, 0],
    [1, 0, 0],
    [0, 1, 0],
    [0, 0, 1],
    [1, 1, 1],
    [1, 2, 3],
]

# Convert to homogeneous coordinates
point_cloud_homogeneous = []
for point in point_cloud:
    point_homogeneous = point.copy()
    point_homogeneous.append(1)
    point_cloud_homogeneous.append(point_homogeneous)

# Define the translation
tx = 2
ty = 10
tz = 100

# Construct the translation matrix
translation_matrix = [
    [1, 0, 0, 0],
    [0, 1, 0, 0],
    [0, 0, 1, 0],
    [tx, ty, tz, 1],
]

# Apply the transformation to our point cloud
translated_points = np.matmul(
    point_cloud_homogeneous,
    translation_matrix)

# Convert to cartesian coordinates
translated_points_xyz = []
for point in translated_points:
    point = np.array(point[:-1])
    translated_points_xyz.append(point)

# Map original to translated point coordinates
# (x0, y0, z0) → (x1, y1, z1)
for i in range(len(point_cloud)):
    point = point_cloud[i]
    translated_point = translated_points_xyz[i]
    print(f'{point} → {list(translated_point)}')