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ROS2 Installation
Follow the Official ROS2 Humble Docs.
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Create Conda Environment
conda create --name vizflyt -y python=3.10.14 conda activate vizflyt python -m pip install --upgrade pip pip uninstall torch torchvision functorch tinycudann
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Install PyTorch
pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118 conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
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Clone the VizFlyt Repository along with modified nerfstudio.
git clone https://github.com/pearwpi/VizFlyt.git cd nerfstudio pip install --upgrade pip setuptools pip install -e .
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Setup vizflyt ros2 workspace
pip install --upgrade "numpy<2" pip install transforms3d gdown
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Download data for running pretrained models
cd vizflyt_ws/src/ chmod +x download_data_and_outputs.sh ./download_data_and_outputs.sh
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Preparing your Environment {Digital Twin Generation using Nerfstudio}
cd vizflyt_ws/src/vizflyt_viewer/ # Training your Environment python scripts/train.py splatfacto --data ./data/washburn-env6-itr0-1fps_nf_format/ --output-dir outputs/washburn-env6-itr0-1fps # Viewing your Environment python scripts/run_viewer.py --load-config ./outputs/washburn-env6-itr0-1fps/washburn-env6-itr0-1fps_nf_format/splatfacto/2025-03-06_032319/config.yml
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Setting Initial Pose and Camera Settings and Render Settings using GUI
- Open the Viewer using,
cd vizflyt_ws/src/vizflyt_viewer/ python scripts/run_viewer.py --load-config ./outputs/washburn-env6-itr0-1fps/washburn-env6-itr0-1fps_nf_format/splatfacto/2025-03-06_032319/config.yml
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and set the following:
- Set Initial Position and Orientation that you want your vehicle to start from using GUI.
- Go to Control Tab, adjust the "Max Res" slider to set the rendering resolution you want at the output.
- Go to Render Tab, adjust the "Default FOV" slider to set the FOV you want at the output.
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After everything is set, press the "Save Camera Pose" button to save your configuration.
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Perception-centric Pedagogical Framework For Autonomous Aerial Robots
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