Library for representing 3D scene graphs
Project description
Spark-DSG
This is the core c++ library that contains the dynamic scene graph data-structure used by Hydra. It also has python bindings.
Change Notes
- 4/22/24: Python bindings and unit tests are built by default. You will need to install relevant dependencies (via
sudo apt install python3-devorrosdep) or disable the bindings and tests via cmake options. - 3/16/24: Updated dependencies to use system libraries for
nlohmann_json. You will need to install it either viasudo apt install nlohmann-json3-devor userosdepto update dependencies.
Acknowledgements and Disclaimer
Acknowledgements: This work was partially funded by the AIA CRA FA8750-19-2-1000, ARL DCIST CRA W911NF-17-2-0181, and ONR RAIDER N00014-18-1-2828.
Disclaimer: Research was sponsored by the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Building for Python
- Install requirements and make a virtual environment:
sudo apt install python3-venv libzmqpp-dev nlohmann-json3-dev
mkdir /path/to/environment
cd /path/to/environment
python3 -m venv dsg # or some other environment name
# you may also want to upgrade pip, though it shouldn't be necessary
# source dsg/bin/activate
# pip install --upgrade pip
- Install the python package
source /path/to/dsg/environment/bin/activate
git clone git@github.com:MIT-SPARK/Spark-DSG.git
pip install ./Spark-DSG
Python Bindings Usage
See this notebook for some examples for the bindings (you'll want to clone the repo, even if you installed from github).
You'll want to install jupyter and jupytext if you want to run it as a notebook, though you can also just run it directly as a python script.
You can find an example scene graph here.
Python API documentation
Generating the python documentation should be as simple as:
source /path/to/dsg/environment/bin/activate
cd doc
pip install sphinx # if you haven't already
make html
python -m http.server # to serve them locally
Building For ROS
This repository is a valid ROS package and should build if placed in a workspace.
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refs/tags/v1.1.2 - Owner: https://github.com/MIT-SPARK
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Access:
public
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Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
wheels.yaml@bca301a3a58fc1943f593af4b7248b86ec06003a -
Trigger Event:
release
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Statement type:
File details
Details for the file spark_dsg-1.1.2-cp38-cp38-macosx_11_0_arm64.whl.
File metadata
- Download URL: spark_dsg-1.1.2-cp38-cp38-macosx_11_0_arm64.whl
- Upload date:
- Size: 557.6 kB
- Tags: CPython 3.8, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/6.1.0 CPython/3.13.7
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Provenance
The following attestation bundles were made for spark_dsg-1.1.2-cp38-cp38-macosx_11_0_arm64.whl:
Publisher:
wheels.yaml on MIT-SPARK/Spark-DSG
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Statement:
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Statement type:
https://in-toto.io/Statement/v1 -
Predicate type:
https://docs.pypi.org/attestations/publish/v1 -
Subject name:
spark_dsg-1.1.2-cp38-cp38-macosx_11_0_arm64.whl -
Subject digest:
1058806767a48904f75f00e100987c3d9b898f94c3a4999f0bce791f43bdc02f - Sigstore transparency entry: 647086300
- Sigstore integration time:
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Permalink:
MIT-SPARK/Spark-DSG@bca301a3a58fc1943f593af4b7248b86ec06003a -
Branch / Tag:
refs/tags/v1.1.2 - Owner: https://github.com/MIT-SPARK
-
Access:
public
-
Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
wheels.yaml@bca301a3a58fc1943f593af4b7248b86ec06003a -
Trigger Event:
release
-
Statement type: