Package: nerfactory
pyRad🤘The all-in-one repo for NeRFsQuickstartSupported FeaturesBenchmarked Model ArchitecturesQuickstartThe quickstart will help you get started with the default vanilla nerf trained on the classic blender lego scene.
For more complex changes (e.g. running with your own data/ setting up a new NeRF graph, please see ourdocs.1. Installation: Setup the environmentThis repository is tested with CUDA 11.3. Make sure to installCondabefore preceding.Installing CondaThis step is fairly self-explanatory, but here are the basic steps. You can also find countless tutorials online.

```
cd /path/to/install/miniconda

mkdir -p miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda3/miniconda.sh
bash miniconda3/miniconda.sh -b -u -p miniconda3
rm -rf miniconda/miniconda.sh
```# Create the python environment
conda create --name pyrad python=3.8.13
conda activate pyrad

# Clone the repo
git clone git@github.com:plenoptix/pyrad.git

# Install dependencies
cd pyrad
pip install -r environment/requirements.txt

# Install pyrad as a library
pip install -e .

# Install library with CUDA support. Change setup.py to `USE_CUDA = True` and then
python setup.py develop

# Install tiny-cuda-nn (tcnn) to use with the graph_instant_ngp.yaml config
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

# Run the test cases
pytest tests2. Getting the dataDownload the originalNeRF datasetand unfold it in the following format. This is for the blender dataset type. We support the major datasets and allow users to create their own dataset, described in detailhere.|─ pyrad/
   ├─ data/
   |  ├─ blender/
   |     ├─ fern/
   |     ├─ lego/
         ...
      |- <dataset_format>/
         |- <scene>
         ...3. Training a modelTo run with all the defaults, e.g. vanilla nerf method with the blender lego image# Run a vanilla nerf model.
python scripts/run_train.py

# Run a faster version with instant ngp using tcnn (without the viewer).
python scripts/run_train.py --config-name=graph_instant_ngp.yaml

# Run with the viewer. However, you'll have to start the viewer server first. (See the docs.)
python scripts/run_train.py --config-name=graph_instant_ngp.yaml viewer.enable=trueWith support forHydra, you can run with other configurations by changing appropriate configs defined inconfigs/or by setting flags via command-line arguments.4. Visualizing training runsWe support multiple methods to visualize training, the default configuration uses Tensorboard. More information on logging can be foundhere.Real-time ViewerWe have developed our own Real-time web viewer, more information can be foundhere. This viewer runs during training and is designed to work with models that have fast rendering pipelines.To enable add the following to your config:viewer:
  enable: trueTensorboardIf you run everything with the default configuration we log all training curves, test images, and other stats. Once the job is launched, you will be able to track training by launching the tensorboard inoutputs/blender_lego/vanilla_nerf/<timestamp>/<events.tfevents>.tensorboard --logdir outputs

# or the following
export TENSORBOARD_PORT=<port>
bash environment/run_tensorboard.shWeights & BiasesWe support logging to weights and biases, to enable add the following to the config:logging:
    writer:
        WandbWriter5. Rendering a trajectories during inferencepython scripts/run_eval.py --method=traj --traj=spiral --output-filename=output.mp4 --config-name=graph_instant_ngp.yaml trainer.resume_train.load_dir=outputs/blender_lego/instant_ngp/2022-07-07_230905/checkpoints6. In-depth guideFor a more in-depth tutorial on how to modify/implement your own NeRF Graph, please see ourwalk-through.Supported FeaturesWe provide the following support strucutures to make life easier for getting started with NeRFs. For a full description, please refer to ourfeatures page.If you are looking for a feature that is not currently supported, please do not hesitate to contact the Plenoptix team!:metal: Support forHydraconfig structure:metal: Support for multiple logging interfaces:metal: Built-in support for profiling code:metal: Benchmarking scripts:metal: Speed up your code with Tiny Cuda NNBenchmarked Model ArchitecturesMethodPSNRNeRF:hourglass_flowing_sand:instant NGP:hourglass_flowing_sand:Mip NeRF:hourglass_flowing_sand: