Awesome
<h2 align="center">Neural Implicit Representations for Physical Parameter Inference from a Single Video</h2> <p align="center"> <a href="https://vision.in.tum.de/members/hofherrf">Florian Hofherr</a><sup>1</sup>   <a href="https://lukaskoestler.com">Lukas Koestler</a><sup>1</sup>   <a href="http://florianbernard.net">Florian Bernard</a><sup>2</sup>   <a href="https://vision.in.tum.de/members/cremers">Daniel Cremers</a><sup>1</sup> </p> <p align="center"> <sup>1</sup>Technical University of Munich    <sup>2</sup>University of Bonn<br> </p> <p align="center"> IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 </p> <p align="center"> <a href="http://arxiv.org/abs/2204.14030">arXiv</a> | <a href="https://florianhofherr.github.io/phys-param-inference/">Project Page</a> </p>Getting Started
You can create an anaconda environment called physParamInference with all the required dependencies by using
conda env create -f environment.yml
conda activate physParamInference
You can download the data using
bash download_data.sh
The script downloads all data used in the paper and stores them into a /data/ folder.
Usage
Training
The training for the different scenarios is run by python training_***.py. The parameters for each scenario are defined in the respective config file in the /configs/ folder.
The results, including checkpoints, as well as the logs are stored in a sub folder of the /experiments/ folder. The path is defined in the config file. You can monitor the progress of the training using tensorboard by calling tensorboard --logidr experiments/path/to/experiment.
Evaluation
For each of the scenarios there is an evaluate_***.ipynb notebook in the /evaluations/ folder that can be used to load and analyze the trained models.