GithubHelp home page GithubHelp logo

mikemiller0401 / nerfren Goto Github PK

View Code? Open in Web Editor NEW

This project forked from bennyguo/nerfren

0.0 0.0 0.0 44 KB

Code release for NeRFReN: Neural Radiance Fields with Reflections (CVPR 2022).

Shell 3.16% Python 96.84%

nerfren's Introduction

NeRFReN: Neural Radiance Fields with Reflections

This is the code release for our CVPR2022 paper, NeRFReN: Neural Radiance Fields with Reflections.

Update

  • 07/28/2022: Initial code release.
  • 08/01/2022: Pretrained models for all RFFR scenes are released.

Setup

  • Install PyTorch>=1.8
  • Install other dependencies: pip install -r requirements.txt
  • Download our Real Forward Facing with Reflections (RFFR) dataset from Google Drive, and extract to load/
  • (Optional) Download pretrained models from Google Drive, and extract to checkpoints/

The correct file structure should be like:

checkpoints/
  |
  -- art1_pretrain/
  |
  -- ...
load/
  |
  -- rffr/
    |
    -- art1/
    |
    -- ...

Training

We provide training scripts for all the 6 RFFR scenes in scripts/nerfren. Run the scripts to perform training:

sh scripts/nerfren/train_art1.sh

To train the NeRF baseline, run scripts/nerf/train.sh and specify the scene as arguments:

sh scripts/nerf/train.sh art1

The training process by default uses all available GPUs. Set CUDA_VISIBLE_DEVICES environment variable to specify the GPUs to be used.

The network checkpoints and visualizations are stored in checkpoints/ by default, and tensorboard logs can be found in runs/.

Testing

The testing process generates images from spiral poses for visualization. To test a pretrained model, run scripts/nerfren/test_pretrain.sh and specify the scene as arguments:

sh scripts/nerfren/test_pretrain.sh art1

To test on our pretrained models, please make sure you have downloaded the checkpoints and organized the files correctly as demonstrated in the Setup section.

The testing results are saved to results/ by default.

Citation

If you find our work useful, please cite:

@InProceedings{Guo_2022_CVPR,
    author    = {Guo, Yuan-Chen and Kang, Di and Bao, Linchao and He, Yu and Zhang, Song-Hai},
    title     = {NeRFReN: Neural Radiance Fields With Reflections},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {18409-18418}
}

Acknowledgement

Part of the code is borrowed or adapted from the following great codebases:

nerfren's People

Contributors

bennyguo avatar mikemiller0401 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.