Lidar data visualization: Difference between revisions

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=== Interpolate Lidar Data ===
=== Lidar Cloud Data to Blender Mesh===
 
Blender need mesh, also nodes and vertices. Thus, the data cloud need to be converted to mesh data. We use the Rolling Ball algorithm of Open3d package.
 
 
[[https://stackoverflow.com/questions/56965268/how-do-i-convert-a-3d-point-cloud-ply-into-a-mesh-with-faces-and-vertices how-do-i-convert-a-3d-point-cloud-ply-into-a-mesh-with-faces-and-vertices]]


[[https://jblindsay.github.io/ghrg/WhiteboxTools/interpolate_lidar.html]]
[[https://jblindsay.github.io/ghrg/WhiteboxTools/interpolate_lidar.html]]

Revision as of 13:58, 30 January 2021

Introduction

Use Lidar data, post analyze it with Python/ Pandas? and use Blender to visualize it.

Theory

The free Lidar data set are available e.g. at Opentopography.org. We use both, the Tif data and LAS data of IT-Ren, Fluxnet site

LAS File Format

LAS@Wikipedia how to print lidar file format las

a) Headers.

b) VLR: Variable Length Record. Include 1) header and 2) payload.

c) Point records. Different point formats 0-10.

LAS in Python

To check if the data points are there

[PyLAS Github]

[Laspy Github] [LasPy]


import numpy as np
import pylas

las = pylas.read( fname )
#np.all(las.user_data == las['user_data'])

point_format = las.point_format
print( point_format )
print( point_format.id )
print( list(point_format.dimension_names) )

from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes(projection='3d')

N=5000
ax.scatter3D(las.X[:N], las.Y[:N], las.Z[:N], c=las.Z[:N], cmap='Greens');

Lidar Cloud Data to Blender Mesh

Blender need mesh, also nodes and vertices. Thus, the data cloud need to be converted to mesh data. We use the Rolling Ball algorithm of Open3d package.


[how-do-i-convert-a-3d-point-cloud-ply-into-a-mesh-with-faces-and-vertices]

[[1]]