nerfstudio/docs/tutorials/quickstart.md

Installation

This repository is tested with CUDA 11.3. Make sure to install Conda before preceding.

Create the python environment

conda create --name nerfstudio python=3.8.13
conda activate nerfstudio
python -m pip install --upgrade pip

Clone the repo

git clone git@github.com:plenoptix/nerfstudio.git

Install dependencies and nerfstudio as a library

cd nerfstudio
pip install -e .

Install tiny-cuda-nn (tcnn) to run instant_ngp

pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

Install tab completion for nerfstudio (bash and zsh)

# If the CLI changes, we can also re-install to synchronize completions
nf-install-cli

Downloading data

Download the original NeRF Blender dataset. We support the major datasets and allow users to create their own dataset, described in detail here.

python scripts/downloads/download_data.py --dataset=blender

Use --help to view all currently available datasets. The resulting script should download and unpack the dataset as follows:

|─ nerfstudio/
   ├─ data/
   |  ├─ blender/
   |     ├─ fern/
   |     ├─ lego/
         ...
      |- <dataset_format>/
         |- <scene>
         ...

Training a model

To run with all the defaults, e.g. vanilla nerf method with the blender lego image

Run a vanilla nerf model.

python scripts/train.py

Run a faster version with instant ngp using tcnn (without the viewer).

python scripts/train.py --config-name=graph_instant_ngp.yaml

Run with the viewer. However, you'll have to start the viewer server first. (See viewer docs)

python scripts/train.py --config-name=graph_instant_ngp.yaml viewer.enable=true

With support for Hydra, you can run with other configurations by changing appropriate configs defined in configs/ or by setting flags via command-line arguments.

Visualizing training runs

If you using a fast NeRF variant (ie. Instant-NGP), we reccomend using our viewer. See our viewer docs for more details. The viewer will allow interactive visualization of training in realtime.

Additionally, if you run everything with the default configuration, by default, we use TensorBoard to 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 in outputs/blender_lego/vanilla_nerf/<timestamp>/<events.tfevents>.

tensorboard --logdir outputs

Rendering a Trajectory

To evaluate the trained NeRF, we provide an evaluation script that allows you to do benchmarking (see our benchmarking workflow) or to render out the scene with a custom trajectory and save the output to a video.

python scripts/eval.py render-trajectory --load-config=outputs/blender_lego/instant_ngp/2022-07-07_230905/config.yml --traj=spiral --output-path=output.mp4

Please note, this quickstart allows you to preform everything in a headless manner. We also provide a web-based viewer that allows you to easily monitor training or render out trajectories. See our viewer docs for more.

FAQ

• TinyCUDA installation errors out with cuda mismatch

(tiny-cuda-error)=

TinyCUDA installation errors out with cuda mismatch

While installing tiny-cuda, you run into: The detected CUDA version mismatches the version that was used to compile PyTorch (10.2). Please make sure to use the same CUDA versions.

Solution:

pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html

• Installation errors, File "setup.py" not found

(pip-install-error)=

Installation errors, File "setup.py" not found

When installing dependencies and nerfstudio with pip install -e ., you run into: ERROR: File "setup.py" not found. Directory cannot be installed in editable mode

Solution: This can be fixed by upgrading pip to the latest version:

python -m pip install --upgrade pip

• Runtime errors, "len(sources) > 0".

(cuda-sources-error)=

Runtime errors, "len(sources) > 0".

When running train.py , an error occurs when installing cuda files in the backend code.

Solution: This is a problem with not being able to detect the correct CUDA version, and can be fixed by updating the CUDA path environment variables:

export CUDA_HOME=/usr/local/cuda
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
export PATH=$PATH:$CUDA_HOME/bin