This repository contains the code for 3D shape fitting to predicted surface normals, as shown in our paper:
FOUND: Foot Optimisation with Uncertain Normals for Surface Deformation using Synthetic Data
Winter Conference on Applications of Computer Vision 2024
Oliver Boyne, Gwangbin Bae, James Charles, and Roberto Cipolla
[arXiv] [project page]
git clone --recurse-submodules http://github.com/OllieBoyne/FOUND- Install dependencies:
pip install -r requirements.txt - Download the pretrained FIND model to
data/find_nfap - Download our benchmark foot dataset to
data/scans - Fit a single scan:
python FOUND/fit.py --exp_name <exp_name> --data_folder <data_folder>
You can use --cfg <file>.yaml to use a config file to set parameters. See args.py for all arguments, and example-cfg.yaml for an example config file.
- Evaluate all of our reconstruction dataset:
python FOUND/eval.py --data_folder <data_folder> --gpus <gpu_indices>
gpu_indices is a space separated list, e.g. --gpus 0 1 2 3
We provide our synthetic foot dataset, SynFoot, which contains 50K synthetic foot scans, with RGB, normals, and masks.
We also provide a benchmark multiview evaluative dataset, Foot3D.
Please check out all of our projects that built into this work!
- FIND - Generative foot model
- Surface Normal Estimation w/ Uncertainty
- BlenderSynth - Synthetic data generation
If you use our work, please cite:
@inproceedings{boyne2024found,
title={FOUND: {F}oot {O}ptimisation with {U}ncertain {N}ormals for Surface {D}eformation using Synthetic Data},
author={Boyne, Oliver, and Bae, Gwangbin, and Charles, James and Cipolla, Roberto},
booktitle={Winter Conference on Applications of Computer Vision (WACV)},
year={2024}
}
If you have any issues with trimesh and shapely, see misc/shapely.md.