Extract geometry features from 3D point clouds
Project description
Extract geometry features from 3D point clouds
Content
The project contains the following folders:
- geo3dfeatures contains source code
- docs contains some mardown files for documentation purpose and images
- examples contains some Jupyter notebooks for describing data
- tests;
pytest
is used to launch several tests from this folder
Additionally, running the code may generate extra subdirectories in a chosen
data repository (./data
, by default).
How to install
This projects runs with Python3, every dependencies are managed through poetry.
Installation from source
$ git clone ssh://git@git.oslandia.net:10022/Oslandia-data/geo3dfeatures.git
$ cd geo3dfeatures
$ virtualenv -p /usr/bin/python3 venv
$ source venv/bin/activate
(venv)$ poetry install
Contribution
See CONTRIBUTING.md.
Run commands
In order to get the available program commands, consider the program help (geo3d -h
):
usage: geo3d [-h] {info,sample,index,featurize,cluster,train,predict} ...
Geo3dfeatures framework for 3D semantic analysis
positional arguments:
{info,sample,index,featurize,cluster,train,predict}
info Describe an input .las file
sample Extract a sample of a .las file
index Index a point cloud file and serialize it
featurize Extract the geometric feature associated to 3D points
cluster Cluster a set of 3D points with a k-means algorithm
train Train a semantic segmentation model
predict Predict 3D point semantic class starting from a
trained model
optional arguments:
-h, --help show this help message and exit
Any further CLI documentation may be printed with geo3d <command> -h
.
Documentation
Some documentation is available, that describes the set of considered geometric features, the fixtures (i.e. dummy datasets) used for test purpose and a practical pipeline use case:
Examples
The following example has been generated starting from
a CANUPO dataset (file scene.xyz
, with 500k points, 50 neighbors and all the features):
Oslandia – 2019-2020
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