nerfstudio/docs/tutorials/quickstart.md

Installation

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

Installing Conda

This step is fairly self-explanatory, but here are the basic steps. You can also find countless tutorials online.

```bash
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 nerfactory python=3.8.13
conda activate nerfactory

Clone the repo

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

Install dependencies

cd nerfactory
pip install -r environment/requirements.txt

Install nerfactory as a library

pip install -e .

Install library with CUDA development 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

Downloading data

Download the original NeRF dataset and 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 detail here.

|─ nerfactory/
   ├─ 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/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 viewer docs)

python scripts/run_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

Or the following:

export TENSORBOARD_PORT=<port>
bash environment/run_tensorboard.sh

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/run_eval.py --method=traj --traj=spiral --output-filename=output.mp4 --config-name=graph_instant_ngp.yaml --checkpoint-dir=outputs/blender_lego/instant_ngp/2022-07-07_230905/checkpoints

Please note, this quickstart allows you to preform everything in a headless manner. We also provide a web-based visualizer 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==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html