In [1]:
An example showing nearest point queries, 
primitive volume sampling, oriented bounding boxes, 
and using PointCloud objects for visualization
import trimesh 
import numpy as np
In [2]:
# load a large- ish PLY model with colors    
mesh = trimesh.load('../models/cycloidal.ply')   
In [3]:
# we can sample the volume of Box primitives
points = mesh.bounding_box_oriented.sample_volume(count=10)
In [4]:
# find the closest point on the mesh to each random point
 triangle_id) = mesh.nearest.on_surface(points)
print('Distance from point to surface of mesh:\n{}'.format(distances))
Distance from point to surface of mesh:
[ 0.05745401  0.16336094  0.03015899  0.00411616  0.26890145  0.05888971
  0.03977633  0.01907153  0.0475863   0.07438896]
In [5]:
# create a PointCloud object out of each (n,3) list of points
cloud_original = trimesh.points.PointCloud(points)
cloud_close    = trimesh.points.PointCloud(closest_points)

# create a unique color for each point
cloud_colors = np.array([trimesh.visual.random_color() for i in points])

# set the colors on the random point and its nearest point to be the same
cloud_original.vertices_color = cloud_colors
cloud_close.vertices_color    = cloud_colors

# create a scene containing the mesh and two sets of points
scene = trimesh.Scene([mesh,

# show the scene wusing