Science Score: 65.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
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○Academic email domains
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✓Institutional organization owner
Organization 3dgeo-heidelberg has institutional domain (uni-heidelberg.de) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (16.8%) to scientific vocabulary
Repository
Voxel Analysis for Point Clouds
Basic Info
Statistics
- Stars: 8
- Watchers: 2
- Forks: 1
- Open Issues: 3
- Releases: 0
Metadata Files
readme.md
Voxel Analysis for Point Clouds
VAPC is a Python library for voxel-based point cloud operations.
3D/4D point clouds are used in many fields and applications. Efficient processing of dense time series of point clouds or large study sites requires tools for automatic analysis. Moreover, methods considering the full 4D (3D space + time) data are being developed in research and need to be made available in an accessible way with flexible integration into existing workflows.
The main objective of VAPC is to bundle and provide different methods of 3D/4D point cloud processing using a voxel-based structure in a dedicated, comprehensive Python library.
Installation • How To Use • Download • Related
Installation
Creating Conda Environments
To avoid negative interactions between installed packages and version conflicts, you should create a conda environment for each new project. You do so by executing: ```bash
First, create new environment
$ conda create --name vapc python=3.10
Then activate the environment using:
$ conda activate vapc
```
Using vapc requires Python 3.10 or higher. Clone and run this application:
```bash
Clone this repository
$ git clone https://github.com/3dgeo-heidelberg/vapc.git
Go into the repository
$ cd vapc
Installing the release version using pip
$ python -m pip install .
OR if editable needed
$ python -m pip install -v --editable .
```
Documentation of software usage
Jupyter Notebooks
Exemplary Jupyter Notebooks are available.
Some useful tools provided by VAPC
- Subsampling of point clouds
- Voxelisation of point clouds
- Computation of voxel based attributes
- Computation of voxel based statistics for existing attributes
- Voxel based 3D masking of point clouds
- Voxel attribute based filtering of point clouds
- Voxel based change detection
- Hierarchical change analysis (using VAPC and py4dgeo)
🎮 Examples
Demo notebooks using methods provided by VAPC
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| Example 1: Voxelisation of point clouds
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Command line VAPC
Using VAPC from the command line with config files is explained in the how to command line.
Download
A small test dataset is currently provided in test data.
Related
3DGeo Research Group, Heidelberg University - Focused on the development of methods for the geographic analysis of 3D/4D point clouds.
License
See LICENSE.
Owner
- Name: 3DGeo Research Group, Heidelberg University
- Login: 3dgeo-heidelberg
- Kind: organization
- Location: Heidelberg, DE
- Website: https://uni-heidelberg.de/3dgeo
- Twitter: 3DGeoHD
- Repositories: 6
- Profile: https://github.com/3dgeo-heidelberg
The 3DGeo Research Group investigates and develops computational methods for the geographic analysis of 3D/4D point clouds.
Citation (citation.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Tabernig
given-names: Ronald
orcid: "0009-0002-3700-899X"
- family-names: Albert
given-names: William
orcid: "0009-0007-6019-4336"
- family-names: Weiser
given-names: Hannah
orcid: "0000-0003-3256-7311"
- family-names: Höfle
given-names: Bernhard
orcid: "0000-0001-5849-1461"
title: "VAPC - Voxel Analysis for Point Clouds"
version: 0.0.1
date-released: 2024-12-01
license: "MIT"
repository-code: "https://github.com/3dgeo-heidelberg/vapc"
GitHub Events
Total
- Issues event: 6
- Watch event: 6
- Delete event: 5
- Public event: 1
- Push event: 51
- Pull request event: 8
- Fork event: 1
- Create event: 4
Last Year
- Issues event: 6
- Watch event: 6
- Delete event: 5
- Public event: 1
- Push event: 51
- Pull request event: 8
- Fork event: 1
- Create event: 4
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 1
- Total pull requests: 5
- Average time to close issues: N/A
- Average time to close pull requests: 4 days
- Total issue authors: 1
- Total pull request authors: 2
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 5
- Average time to close issues: N/A
- Average time to close pull requests: 4 days
- Issue authors: 1
- Pull request authors: 2
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- tabernig (4)
- han16nah (1)
Pull Request Authors
- tabernig (4)
- han16nah (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- ipykernel *
- laspy [lazrs]>=2.3.0
- numpy *
- pandas *
- plyfile *
- scipy *
- toml *
- ipykernel * development
- nbsphinx * development
- nbsphinx-link * development
- pandas * development
- pytest * development
- pytest-cov * development
- sphinx * development
- sphinx-autodoc-typehints * development
- sphinx_mdinclude * development
- sphinx_rtd_theme * development