My Readme

wandb

1. Create a new project in wandb
2. conda activate neuralvolumes
3. wandb login
4. paste the authorization key of the new created project
5. fill up the wandb part in the env.json file

conda environment

Install a package

1. conda activate neuralvolumes
2. conda install -c <conda-source> <package-name>
3. conda env export -c pytorch -c nvidia/label/cuda-11.6.2 -f environment.yml --from-history
4. Check the changes via git of the environment.yml file and commit if OK

Update conda env

1. conda activate neuralvolumes
2. conda env update --file environment.yml

Deployment scripts

Server init

1. cd
2. create experiments folder
3. create shared folder
4. create first version git pull
5. ln -s experiments folder
6. conda env create
7. message that data is expected in experiments file
8. message that env.json needs to be placed in shared file

Deploy

1. git checkout -b main
2. git pull
3. conda update -f environment.yml
4. python train.py or python render.py

Neural Volumes - Readme from forked Repository

This repository contains training and evaluation code for the paper Neural Volumes. The method learns a 3D volumetric representation of objects & scenes that can be rendered and animated from only calibrated multi-view video.

Citing Neural Volumes

If you use Neural Volumes in your research, please cite the paper:

@article{Lombardi:2019,
 author = {Stephen Lombardi and Tomas Simon and Jason Saragih and Gabriel Schwartz and Andreas Lehrmann and Yaser Sheikh},
 title = {Neural Volumes: Learning Dynamic Renderable Volumes from Images},
 journal = {ACM Trans. Graph.},
 issue_date = {July 2019},
 volume = {38},
 number = {4},
 month = jul,
 year = {2019},
 issn = {0730-0301},
 pages = {65:1--65:14},
 articleno = {65},
 numpages = {14},
 url = {http://doi.acm.org/10.1145/3306346.3323020},
 doi = {10.1145/3306346.3323020},
 acmid = {3323020},
 publisher = {ACM},
 address = {New York, NY, USA},
}

File Organization

The root directory contains several subdirectories and files:

data/ --- custom PyTorch Dataset classes for loading included data
eval/ --- utilities for evaluation
experiments/ --- location of input data and training and evaluation output
models/ --- PyTorch modules for Neural Volumes
render.py --- main evaluation script
train.py --- main training script

Requirements

• Python (3.6+)
  • PyTorch (1.2+)
  • NumPy
  • Pillow
  • Matplotlib
• ffmpeg (in PATH, needed to render videos)

How to Use

There are two main scripts in the root directory: train.py and render.py. The scripts take a configuration file for the experiment that defines the dataset used and the options for the model (e.g., the type of decoder that is used).

A sample set of input data is provided in the v0.1 release and can be downloaded here and extracted into the root directory of the repository. experiments/dryice1/data contains the input images and camera calibration data, and experiments/dryice1/experiment1 contains an example experiment configuration file (experiments/dryice1/experiment1/config.py).

To train the model:

python train.py experiments/dryice1/experiment1/config.py

To render a video of a trained model:

python render.py experiments/dryice1/experiment1/config.py Render

License

See the LICENSE file for details.