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Training and Evaluation Code for Neural Volumes
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README.md

Neural Volumes

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.

Neural Volumes

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.

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