vdbfusion

C++/Python Sparse Volumetric TSDF Fusion

https://github.com/prbonn/vdbfusion

Science Score: 85.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 1 DOI reference(s) in README
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Academic publication links
Links to: mdpi.com
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Committers with academic emails
2 of 8 committers (25.0%) from academic institutions
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Institutional organization owner
Organization prbonn has institutional domain (www.ipb.uni-bonn.de)
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (14.3%) to scientific vocabulary

Keywords

3dmapping lidar-point-cloud rgbd tsdf-fusion tsdf-volume

Keywords from Contributors

3d-mapping icp lidar-slam ros ros2 slam

Last synced: 6 months ago · JSON representation ·

Repository

C++/Python Sparse Volumetric TSDF Fusion

Basic Info

Host: GitHub
Owner: PRBonn
License: mit
Language: C++
Default Branch: main
Homepage:
Size: 9.33 MB

Statistics

Stars: 573
Watchers: 9
Forks: 61
Open Issues: 5
Releases: 2

Topics

3dmapping lidar-point-cloud rgbd tsdf-fusion tsdf-volume

Created about 4 years ago · Last pushed about 1 year ago

Metadata Files

Readme License Citation

VDBFusion: Flexible and Efficient TSDF Integration

example

This is a small utility library that implements the VDBFusion algorithm, similar to TSDF-based reconstruction pipelines but using a different data-structure (VDB).

Installation

Take a seat and relax, you only need to:

shell pip install vdbfusion

If you plan to use our C++ API then you should build this project from source. More details in the Installation instructions.

The ROS-1 C++ wrapper for this library is available at https://github.com/PRBonn/vdbfusion_ros

Usage

The code shown below is not intended to be copy pasted but rather be a spiritual guide for developers. If you really want to give this library a try you should consider checking the standalone Python, Notebooks, and C++ examples.

Data loading

NOTE: This step is not mandatory. Our API only expects points and poses but this is the easiest way to deal with 3D data.

Python	C++
```python class Dataset: def __init__(self, args, *kwargs): # Initialize your dataset here .. def __len__(self) -> int: return len(self.n_scans) def __getitem__(self, idx: int): # Returns a PointCloud(np.array(N, 3)) # and sensor origin(Eigen::Vector3d) # in the global coordinate frame. return points, origin ```	```c++ class Dataset { // Initialize your dataset here .. Dataset(...); // Return length of the dataset std::size_t size() const { return n_scans_; } // Returns a Cloud(std::vector) // and the sensor origin(Eigen::Vector3d) in the // global coordinate frame. std::tuple operator[](int idx) const; }; ```

TSDF Fusion pipeline

Python	C++
```python import vdbfusion vdb_volume = vdbfusion.VDBVolume(voxel_size, sdf_trunc, space_carving dataset = Dataset(...) for scan, origin in dataset: vdb_volume.integrate(scan, origin) ```	```cpp #include "vdbfusion/VDBVolume.h" vdb_fusion::VDBVolume vdb_volume(voxel_size, sdf_trunc, space_carving); const auto dataset = Dataset(...); for (const auto& [scan, origin] : iterable(dataset)) { vdb_volume.Integrate(scan, origin); } ```

Visualization

For visualization you can use any 3D library you like. For this example we are going to be using Open3D. If you are using the Python API make sure to pip install open3d before trying this snippet.

Python	C++
```python import open3d as o3d # Extract triangle mesh (numpy arrays) vert, tri = vdb_volume.extract_triangle_mesh() # Visualize the results mesh = o3d.geometry.TriangleMesh( o3d.utility.Vector3dVector(vert), o3d.utility.Vector3iVector(tri), ) mesh.compute_vertex_normals() o3d.visualization.draw_geometries([mesh]) ```	```cpp #include // Extract triangle mesh (Eigen). auto [verts, tris] = vdb_volume.ExtractTriangleMesh(); // Visualize the results auto mesh = o3d::geometry::TriangleMesh( verts, tris, ) mesh.ComputeVertexNormals() o3d::visualization::DrawGeometries({&mesh}) ```

LICENSE

The LICENSE can be found at the root of this repository. It only applies to the code of VDBFusion but not to its 3rdparty dependencies. Please make sure to check the licenses in there before using any form of this code.

Credits

I would like to thank the Open3D and OpenVDB authors and contributors for making their implementations open source which inspired, helped and guided the implementation of the VDBFusion library.

Citation

If you use this library for any academic work, please cite the original paper.

bibtex @article{vizzo2022sensors, author = {Vizzo, Ignacio and Guadagnino, Tiziano and Behley, Jens and Stachniss, Cyrill}, title = {VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data}, journal = {Sensors}, volume = {22}, year = {2022}, number = {3}, article-number = {1296}, url = {https://www.mdpi.com/1424-8220/22/3/1296}, issn = {1424-8220}, doi = {10.3390/s22031296} }

Owner

Name: Photogrammetry & Robotics Bonn
Login: PRBonn
Kind: organization
Email: cyrill.stachniss@igg.uni-bonn.de
Location: Bonn

Website: www.ipb.uni-bonn.de
Repositories: 41
Profile: https://github.com/PRBonn

Photogrammetry & Robotics Lab at the University of Bonn

Citation (CITATION.cff)

cff-version: 1.2.0
preferred-citation:
  title: "VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data"
  doi: "10.3390/s22031296"
  year: "2022"
  type: article
  journal: "Sensors"
  url: https://www.mdpi.com/1424-8220/22/3/1296
  authors:
    - family-names: Vizzo
      given-names: Ignacio
    - family-names: Guadagnino
      given-names: Tiziano
    - family-names: Behley
      given-names: Jens
    - family-names: Stachniss
      given-names: Cyrill

GitHub Events

Total

Issues event: 8
Watch event: 80
Issue comment event: 10
Push event: 1
Pull request event: 1
Fork event: 8

Last Year

Issues event: 8
Watch event: 80
Issue comment event: 10
Push event: 1
Pull request event: 1
Fork event: 8

Committers

Last synced: 11 months ago

All Time

Total Commits: 26
Total Committers: 8
Avg Commits per committer: 3.25
Development Distribution Score (DDS): 0.423

Past Year

Commits: 3
Committers: 2
Avg Commits per committer: 1.5
Development Distribution Score (DDS): 0.333

Top Committers

Name	Email	Commits
Ignacio Vizzo	i**o@g**m	15
Saurabh Gupta	s**2@g**m	4
Sumanth Nagulavancha	8****7	2
luarzou	s**o@u**e	1
Wei Zhang	z**5@g**m	1
SamThomas	s**l@g**m	1
Jan Kuhlmann	3****E	1
Benedikt Mersch	m**h@i**e	1

Committer Domains (Top 20 + Academic)

igg.uni-bonn.de: 1 uni-bonn.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 35
Total pull requests: 18
Average time to close issues: about 1 month
Average time to close pull requests: about 1 month
Total issue authors: 30
Total pull request authors: 10
Average comments per issue: 2.89
Average comments per pull request: 2.11
Merged pull requests: 11
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 5
Pull requests: 2
Average time to close issues: about 2 months
Average time to close pull requests: 1 day
Issue authors: 5
Pull request authors: 1
Average comments per issue: 0.6
Average comments per pull request: 1.0
Merged pull requests: 2
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

qpc001 (5)
Willyzw (2)
lin-name (1)
Rockarel (1)
MohamedEllaithy42 (1)
YM5149 (1)
JunyuanDeng (1)
SunshineWYC (1)
changyuan79 (1)
swarmt (1)
maulanaazhari (1)
ignotuspeverel (1)
zang09 (1)
waveleaf27 (1)
kuwan2e (1)

Pull Request Authors

saurabh1002 (6)
swarmt (3)
sumanthrao1997 (3)
borongyuan (2)
M2-TE (2)
cosama (2)
mehermvr (2)
luarzou (1)
Willyzw (1)
anja-sheppard (1)

Top Labels

Issue Labels

Pull Request Labels

enhancement (2)

Packages

Total packages: 1
Total downloads:
- pypi 797 last-month

Total dependent packages: 0
Total dependent repositories: 2
Total versions: 5
Total maintainers: 1

pypi.org: vdbfusion

3D Volumetric Surface Reconstruction using the VDB data structure

Documentation: https://vdbfusion.readthedocs.io/
License: MIT License
Latest release: 0.1.6
published almost 4 years ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 2
Downloads: 797 Last month

Rankings

Dependent packages count: 10.0%

Dependent repos count: 11.6%

Average: 12.4%

Downloads: 15.7%

Maintainers (1)

nachovizzo

Last synced: 6 months ago

Dependencies

docker/builder/Dockerfile docker

ubuntu 20.04 build

docker/pip/Dockerfile docker

ubuntu 20.04 build

docker-compose.yml docker

gitlab.ipb.uni-bonn.de 4567/ipb-team/ipb-tools/vdbfusion
gitlab.ipb.uni-bonn.de 4567/ipb-team/ipb-tools/vdbfusion/pip_builder

dev-requirements.txt pypi

pytest * development

examples/python/requirements.txt pypi

PyYAML *
argh *
diskcache >=5.3.0
easydict *
natsort *
numpy *
open3d *
pandas *
scipy *
tqdm *
trimesh *
vdbfusion *

requirements.txt pypi

numpy *

pyproject.toml pypi

setup.py pypi

vdbfusion

Science Score: 85.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

VDBFusion: Flexible and Efficient TSDF Integration

Installation

Usage

Data loading

TSDF Fusion pipeline

Visualization

LICENSE

Credits

Citation

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: vdbfusion

Rankings

Maintainers (1)

Dependencies