Haofei Xu
·
Anpei Chen
·
Yuedong Chen
·
Christos Sakaridis
·
Yulun Zhang
Marc Pollefeys
·
Andreas Geiger
·
Fisher Yu
MuRF supports multiple different baseline settings.
MuRF achieves state-of-the-art performance under various evaluation settings.
Our code is developed based on pytorch 1.10.1, CUDA 11.3 and python 3.8.
We recommend using conda for installation:
conda create -n murf python=3.8
conda activate murf
pip install -r requirements.txt
The models are hosted on Hugging Face 🤗 : https://huggingface.co/haofeixu/murf
Model details can be found at MODEL_ZOO.md.
The datasets used to train and evaluate our models are detailed in DATASETS.md
The evaluation scripts used to reproduce the numbers in our paper are detailed in scripts/*_evaluate.sh.
The rendering scripts are detailed in scripts/*_render.sh.
@article{xu2023murf,
title={MuRF: Multi-Baseline Radiance Fields},
author={Xu, Haofei and Chen, Anpei and Chen, Yuedong and Sakaridis, Christos and Zhang, Yulun and Pollefeys, Marc and Geiger, Andreas and Yu, Fisher},
journal={arXiv:2312.04565},
year={2023}
}
This repo is heavily based on MatchNeRF, thanks Yuedong Chen for this fantastic work. This project also borrows code from several other repos: GMFlow, UniMatch, latent-diffusion, MVSNeRF, IBRNet, ENeRF and cross_attention_renderer. We thank the original authors for their excellent work.