Tanks and Temples:

• Playground:

• Truck:

• M60:

• Train:

Mip-NeRF-360 Benchmark:

• Bicycle:

• Stump:

• Kitchen:

• Bonsai:

• Garden:

• Counter:

• Room:

San Francisco Mission Bay (dataset released by Block-NeRF):

• Training splits:

  oct2_124_300_frames_trim.mp4

• Rotation:

  rotation.mov

• [2023.2.27] A major update of our repository with better performance and full code release.
• [2022.12.23] Released several weeks' NeRF. Too many papers pop out these days so the update speed is slow.
• [2022.9.12] Training Block-NeRF on the Waymo dataset, reaching PSNR 24.3.
• [2022.8.31] Training Mega-NeRF on the Waymo dataset, loss still NAN.
• [2022.8.24] Support the full Mega-NeRF pipeline.
• [2022.8.18] Support all previous papers in weekly classified NeRF.
• [2022.8.17] Support classification in weekly NeRF.
• [2022.8.16] Support evaluation scripts and data format standard. Getting some results.
• [2022.8.13] Add estimated camera pose and release a better dataset.
• [2022.8.12] Add weekly NeRF functions.
• [2022.8.8] Add the NeRF reconstruction code and doc for custom purposes.
• [2022.7.28] The data preprocess script is finished.
• [2022.7.20] This project started!

1. Clone this repository. Use depth == 1 to avoid download a large history.

   git clone --depth=1 [email protected]:sjtuytc/LargeScaleNeRFPytorch.git
   mkdir data
   mkdir logs

2. Create conda environment.

   conda create -n large-scale-nerf python=3.9
   conda activate large-scale-nerf

3. Install pytorch and other libs. Make sure your Pytorch version is compatible with your CUDA.

   pip install --upgrade pip
   conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
   pip install -r requirements.txt

4. Install grid-based operators to avoid running them every time, cuda lib required. (Check via "nvcc -V" to ensure that you have a latest cuda.)

   apt-get install g++ build-essential  # ensure you have g++ and other build essentials, sudo access required.
   cd FourierGrid/cuda
   python setup.py install
   cd ../../

5. Install other libs used for reconstructing custom scenes. This is only needed when you need to build your scenes.

   sudo apt-get install colmap
   sudo apt-get install imagemagick  # required sudo accesss
   conda install pytorch-scatter -c pyg  # or install via https://github.com/rusty1s/pytorch_scatter

   You can use laptop version of COLMAP as well if you do not have access to sudo access on your server. However, we found if you do not set up COLMAP parameters properly, you would not get the SOTA performance.

• Disclaimer: users are required to get permission from the original dataset provider. Any usage of the data must obey the license of the dataset owner.

(1) Unbounded Tanks & Temples. Download data from here. Then unzip the data.

cd data
gdown --id 11KRfN91W1AxAW6lOFs4EeYDbeoQZCi87
unzip tanks_and_temples.zip
cd ../

(2) The Mip-NeRF-360 dataset.

cd data
wget http://storage.googleapis.com/gresearch/refraw360/360_v2.zip
mkdir 360_v2
unzip 360_v2.zip -d 360_v2
cd ../

(3) San Fran Cisco Mission Bay. What you should know before downloading the data:

• Our processed waymo data is significantly smaller than the original version (19.1GB vs. 191GB) because we store the camera poses instead of raw ray directions. Besides, our processed data is more friendly for Pytorch dataloaders. Download the data in the Google Drive. You may use gdown to download the files via command lines. If you are interested in processing the raw waymo data on your own, please refer to this doc.

The downloaded data would look like this:

data
   |
   |——————360_v2                                    // the root folder for the Mip-NeRF-360 benchmark
   |        └——————bicycle                          // one scene under the Mip-NeRF-360 benchmark
   |        |         └——————images                 // rgb images
   |        |         └——————images_2               // rgb images downscaled by 2
   |        |         └——————sparse                 // camera poses
   |        ...
   |——————tanks_and_temples                         // the root folder for Tanks&Temples
   |        └——————tat_intermediate_M60             // one scene under Tanks&Temples
   |        |         └——————camera_path            // render split camera poses, intrinsics and extrinsics
   |        |         └——————test                   // test split
   |        |         └——————train                  // train split
   |        |         └——————validation             // validation split
   |        ...
   |——————pytorch_waymo_dataset                     // the root folder for San Fran Cisco Mission Bay
   |        └——————cam_info.json                    // extracted cam2img information in dict.
   |        └——————coordinates.pt                   // global camera information used in Mega-NeRF, deprecated
   |        └——————train                            // train data
   |        |         └——————metadata               // meta data per image (camera information, etc)
   |        |         └——————rgbs                   // rgb images
   |        |         └——————split_block_train.json // split block informations
   |        |         └——————train_all_meta.json    // all meta informations in train folder
   |        └——————val                              // val data with the same structure as train

You only need to run "python run_FourierGrid.py" to finish the train-test-render cycle. Explanations of some arguments:

--program: the program to run, normally --program train will be all you need.
--config: the config pointing to the scene file, e.g., --config FourierGrid/configs/tankstemple_unbounded/truck_single.py.
--num_per_block: number of blocks used in large-scale NeRFs, normally this is set to -1, unless specially needed.
--render_train: render the trained model on the train split.
--render_train: render the trained model on the test split.
--render_train: render the trained model on the render split.
--exp_id: add some experimental ids to identify different experiments. E.g., --exp_id 5.
--eval_ssim / eval_lpips_vgg: report SSIM / LPIPS(VGG) scores.

While we list major of the commands in scripts/train_FourierGrid.sh, we list some of commands below for better reproducibility.

# Unbounded tanks and temples
python run_FourierGrid.py --program train --config FourierGrid/configs/tankstemple_unbounded/playground_single.py --num_per_block -1 --render_train --render_test --render_video --exp_id 57
python run_FourierGrid.py --program train --config FourierGrid/configs/tankstemple_unbounded/train_single.py --num_per_block -1 --render_train --render_test --render_video --exp_id 12
python run_FourierGrid.py --program train --config FourierGrid/configs/tankstemple_unbounded/truck_single.py --num_per_block -1 --render_train --render_test --render_video --exp_id 4
python run_FourierGrid.py --program train --config FourierGrid/configs/tankstemple_unbounded/m60_single.py --num_per_block -1 --render_train --render_test --render_video --exp_id 6

# 360 degree dataset
python run_FourierGrid.py --program train --config FourierGrid/configs/nerf_unbounded/room_single.py --num_per_block -1 --eval_ssim --eval_lpips_vgg --render_train --render_test --render_video --exp_id 9
python run_FourierGrid.py --program train --config FourierGrid/configs/nerf_unbounded/stump_single.py --num_per_block -1 --eval_ssim --eval_lpips_vgg --render_train --render_test --render_video --exp_id 10
python run_FourierGrid.py --program train --config FourierGrid/configs/nerf_unbounded/bicycle_single.py --num_per_block -1 --eval_ssim --eval_lpips_vgg --render_train --render_test --render_video --exp_id 11
python run_FourierGrid.py --program train --config FourierGrid/configs/nerf_unbounded/bonsai_single.py --num_per_block -1 --eval_ssim --eval_lpips_vgg --render_train --render_test --render_video --exp_id 3
python run_FourierGrid.py --program train --config FourierGrid/configs/nerf_unbounded/garden_single.py --num_per_block -1 --eval_ssim --eval_lpips_vgg --render_train --render_test --render_video --exp_id 2
python run_FourierGrid.py --program train --config FourierGrid/configs/nerf_unbounded/kitchen_single.py --num_per_block -1 --eval_ssim --eval_lpips_vgg --render_train --render_test --render_video --exp_id 2
python run_FourierGrid.py --program train --config FourierGrid/configs/nerf_unbounded/counter_single.py --num_per_block -1 --eval_ssim --eval_lpips_vgg --render_train --render_test --render_video --exp_id 2

# San Francisco Mission Bay dataset
python run_FourierGrid.py --program train --config FourierGrid/configs/waymo/waymo_no_block.py --num_per_block 100 --render_video --exp_id 30

The old version of Block-NeRF is still provided to serve as a baseline, but it will be deprecated soon. We will mainly work on grid-based models later because they are simple and fast. Run the following command to reproduce the old Block-NeRF experiments:

bash scripts/block_nerf_train.sh
bash scripts/block_nerf_eval.sh

1. Put your images under data folder. The structure should be like:

   data
      |——————Madoka          // Your folder name here.
      |        └——————source // Source images should be put here.
      |                 └——————---|1.png
      |                 └——————---|2.png
      |                 └——————---|...

   The sample data is provided in our Google drive folder. The Madoka and Otobai can be found at this link.

2. Run COLMAP to reconstruct scenes. This would probably cost a long time.

   python FourierGrid/tools/imgs2poses.py data/Madoka

   You can replace data/Madoka by your data folder. If your COLMAP version is larger than 3.6 (which should not happen if you use apt-get), you need to change export_path to output_path in the colmap_wrapper.py.

3. Training NeRF scenes.

   python FourierGrid/run_FourierGrid.py --config configs/custom/Madoka.py

   You can replace configs/custom/Madoka.py by other configs.

4. Validating the training results to generate a fly-through video.

   python FourierGrid/run_FourierGrid.py --config configs/custom/Madoka.py --render_only --render_video --render_video_factor 8