1. To create a virtual environment use these commands

python3 -m venv venv
. venv/bin/activate

2. Additionally please install PyTorch, we tested on torch1.12.1+cu116

# torch 1.12.1+cu116
pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116

3. Install the requirements file with

pip install -r requirements.txt

A README of the dataset can be found in the loaders folder.

Datasets can also be downloaded using the download_datasets.py script. With flags --scenes o1 o2 o3, replacing o1, o2 and o3 with scenes you want to download. You can use shorthand all, captured or simulated or otherwise specify scenes by their names.

Datasets can also be downloaded from Dropbox.

Please unzip it and have it in the root directry. If you want to install it elsewhere, please manually change the directories in the config.

You can train TransientNeRF by specifying a config and view number of the scene you want to train on, for example for lego two views, you would:

python train.py -c="./configs/train/simulated/lego_two_views.ini"

To see the summary during training, run the following

tensorboard --logdir=./results/ --port=6006

You can then evaluate the same scene (to get quantitative and image results) with

python eval.py -c="./configs/train/captured/cinema_two_views.ini" -tc="./configs/test/simulated/lego_quantitative.ini" --checkpoint_dir=<trained model directory root>

• train.py main training script containing training loop
• utils.py contains rendering function render_transient, which calls the occgrid to generate sample points and then samples them
• misc/transient_volrend.py called by utils contains the rendering code i.e. the whole image formation model, including convolution with pulse in mapping_dist_to_bin_mitsuba

I have realised that the models for the paper (the captured ones) were a bit undertrained (150k iterations), the configs thus train for longer than suggested in the paper (500k iterations). The difference is mainly important for the 5 views case, where PSNR increases by ~3/4dB.

@inproceedings{malik2023transient,
  title = {Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction}, 
  author = {Anagh Malik and Parsa Mirdehghan and Sotiris Nousias and Kiriakos N. Kutulakos and David B. Lindell},
  journal = {NeurIPS},
  year = {2023}
}

We thank NerfAcc for their implementation of Instant-NGP.