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murf's Introduction

MuRF: Multi-Baseline Radiance Fields

Haofei Xu · Anpei Chen · Yuedong Chen · Christos Sakaridis · Yulun Zhang
Marc Pollefeys · Andreas Geiger · Fisher Yu

CVPR 2024

murf_teaser_video.mp4

MuRF supports multiple different baseline settings.

Logo

MuRF achieves state-of-the-art performance under various evaluation settings.

Installation

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

Model Zoo

The models are hosted on Hugging Face 🤗 : https://huggingface.co/haofeixu/murf

Model details can be found at MODEL_ZOO.md.

Datasets

The datasets used to train and evaluate our models are detailed in DATASETS.md

Evaluation

The evaluation scripts used to reproduce the numbers in our paper are detailed in scripts/*_evaluate.sh.

Rendering

The rendering scripts are detailed in scripts/*_render.sh.

Training

The training scripts are detailed in scripts/*_train.sh.

Citation

@inproceedings{xu2024murf,
      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},
      booktitle={CVPR},
      year={2024}
    }

Acknowledgements

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.

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murf's Issues

DTU Large Baseline dataset

Hello! i've attempted to follow your instructions, but i can't seem to find where the calibration folder in the dataset is created from! it's not present in the RegNeRF guide you've given link to.

Great paper, can't wait to try it out!

Yiftach

About the training datasets

Thanks for your excellent work! I would like to ask if the training and testing dataset were conducted separately on the DTU, LLFF, and RealEstate datasets. For example, the model trained on LLFF and then tested on LLFF.

Train with other datasets

Hello, I have some questions about model training that I would like to ask you.
Why the loss I trained with the dtu dataset cannot converge?
How should I use other datasets for training?
Is it easy to train?
Will the effect be the same as the effect you showed?

Training on datasets other than DTU

Thanks @haofeixu, for making this amazing work publicly available!
I'm just trying to run training on Realestate10k datasets (I got the full set already), but I noticed that the config yaml files for training on realestate or mixed datasets are not there. Will it be shared? If it's not, it's fine, I just want to ask if the learning rates, optimizer , loss weights the same to the setting used for DTU.

Thanks!

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