3D visualization point cloud

Visualize with Mayavi

First install mayavi using anaconda and open the command line interface

conda install mayavi

If the python version is py3, an error will occur, and the python2.7 version needs to be installed:

conda create -n py2 python=2

Then install mayavi in ​​the py2 environment:

conda install -n py2 mayavi

If the VTK version does not match, update VTK:

conda update -n py2 vtk

The directory of py2 is in D:\Anaconda\envs\py2, then use pycharm to switch the version:

import numpy as np

def viz_mayavi(points, vals="distance"):
    x = points[:, 0]  # x position of point
    y = points[:, 1]  # y position of point
    z = points[:, 2]  # z position of point
    # r = lidar[:, 3]  # reflectance value of point
    d = np.sqrt(x ** 2 + y ** 2)  # Map Distance from sensor

    # Plot using mayavi -Much faster and smoother than matplotlib
    import mayavi.mlab

    if vals == "height":
        col = z
    else:
        col = d

    fig = mayavi.mlab.figure(bgcolor=(0, 0, 0), size=(640, 360))
    mayavi.mlab.points3d(x, y, z,
                         col,          # Values used for Color
                         mode="point",
                         colormap='spectral', # 'bone', 'copper', 'gnuplot'
                         # color=(0, 1, 0),   # Used a fixed (r,g,b) instead
                         figure=fig,
                         )
    mayavi.mlab.show()

points = np.loadtxt('0000000000.txt')
viz_mayavi(points)

Visualize with Matplotlib

Pycharm switches back to the py3 version:

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

points = np.loadtxt('0000000000.txt')
skip = 20   # Skip every n points

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
point_range = range(0, points.shape[0], skip) # skip points to prevent crash
ax.scatter(points[point_range, 0],   # x
           points[point_range, 1],   # y
           points[point_range, 2],   # z
           c=points[point_range, 2], # height data for color
           cmap='spectral',
           marker="x")
ax.axis('scaled')  # {equal, scaled}
plt.show()

Special note: This article has taken notes for my study.
Specific reference:http://ronny.rest/tutorials/module/pointclouds_01/point_cloud_birdseye/