GP-NeRF

This repository contains the code needed to train GP-NeRF.

Note: This is a preliminary release and there may still be outstanding bugs.

Setup

Create new conda env (pytorch-version, CUDA)

conda create -n gpnerf python=3.9
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r requirements.txt

Install tiny-cuda-nn

tiny-cuda-nn$ cd bindings/torch
tiny-cuda-nn/bindings/torch$ python setup.py install

Pretrained Models

coming soon.

Datasets

Please refer to Mega-NeRF for downloading the datasets.

MegaNeRF
├── Mill19
│   ├── building
│   │   ├── building-pixsfm
│   ├── rubble
│   │   ├── rubble-pixsfm
│   ├── building_chunk-1 (auto processed by scripts when training if use "--dataset_type filesystem". You can choose another path to save.)
│   ├── rubble_chunk-1
├── Quad6k (the same as above)
│   ├── quad
├── UrbanScene3D (the same as above)
│   ├── residence
│   ├── sci-art
│   ├── campus

Training

python gp_nerf/train.py --config_file configs/${DATASET_NAME}.yml --exp_name $EXP_PATH --dataset_path $DATASET_PATH --chunk_paths $CHUNK_PATH

At the first time of running, it takes some times to write the chunk into the disk.

Evaluation

python gp_nerf/eval.py --config_file configs/${DATASET_NAME}.yaml  --exp_name $EXP_NAME --dataset_path $DATASET_PATH  --ckpt_path  $ckpt_path

Acknowledgements

Large parts of this codebase are based on existing work in the Mega-NeRF and torch-ngp repositories.