| examples | ||
| include | ||
| mesh_intersection | ||
| src | ||
| .gitignore | ||
| LICENSE | ||
| README.md | ||
| setup.py | ||
Mesh Self Intersection
This package provides a PyTorch module that can efficiently (1) detect and (2) penalize (self-)intersections for a triangular mesh.
Table of Contents
License
Software Copyright License for non-commercial scientific research purposes. Please read carefully the terms and conditions and any accompanying documentation before you download and/or use the SMPL-X/SMPLify-X model, data and software, (the "Model & Software"), including 3D meshes, blend weights, blend shapes, textures, software, scripts, and animations. By downloading and/or using the Model & Software (including downloading, cloning, installing, and any other use of this github repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Model & Software. Any infringement of the terms of this agreement will automatically terminate your rights under this License.
Description
This repository provides a PyTorch wrapper around a CUDA kernel that implements the method described in Maximizing parallelism in the construction of BVHs, octrees, and k-d trees. More specifically, given an input mesh it builds a BVH tree for each one and queries it for self-intersections. Moreover, we provide a conical 3D distance field based loss for resolving the interpenetrations, as in Capturing Hands in Action using Discriminative Salient Points and Physics Simulation.
Please note that in the current implementation, for batching one needs to provide meshes with the same number of faces. Moreover, the code by default works for self-penetrations of a body mesh. The module can be used also for inter-penetrations of different meshes - for this the easiest and naive approach (without additional bookkeeping) is to fuse all meshes in a single mesh and treat inter-penetrations as self-penetrations.
Installation
Before installing anything please make sure to set the environment variable
$CUDA_SAMPLES_INC to the path that contains the header helper_math.h (provided by NVidia SDK samples).
To install the module you can:
- Clone this repository and install it using the setup.py script:
git clone https://github.com/vchoutas/torch-mesh-isect
python setup.py install
- Install from PyPi the pre-compiled version for 1.1.0 and CUDA 10.0 simply run:
pip install torch-mesh-isect
- To install the module and the rendering dependencies run:
pip
install torch-mesh-isect[all]
Examples
- Collision Detection: Given an
input mesh file, detect and plot all the collisions. Use:
python examples/detect_and_plot_collisions.py PATH_TO_MESH - Batch Collision resolution: Resolve self-penetrations for a batch of body models. To run use:
where PKL_FN* are the filenames of the .pkl files that can be downloaded here and contain the parameters for each body model.python examples/batch_smpl_untangle.py --coll_loss_weight=$WEIGHT --param_fn PKL_FN1 PKL_FN2 ... PKL_FNN
Dependencies
Example dependencies
Optional Dependencies
Citation
If you find this code useful in your research then please cite the following two works, for detecting and penalizing mesh intersections accordingly:
@inproceedings{Karras:2012:MPC:2383795.2383801,
author = {Karras, Tero},
title = {Maximizing Parallelism in the Construction of BVHs, Octrees, and K-d Trees},
booktitle = {Proceedings of the Fourth ACM SIGGRAPH / Eurographics Conference on High-Performance Graphics},
series = {EGGH-HPG'12},
year = {2012},
isbn = {978-3-905674-41-5},
location = {Paris, France},
pages = {33--37},
numpages = {5},
url = {https://doi.org/10.2312/EGGH/HPG12/033-037},
doi = {10.2312/EGGH/HPG12/033-037},
acmid = {2383801},
publisher = {Eurographics Association},
address = {Goslar Germany, Germany},
}
@article{Tzionas:IJCV:2016, title = {Capturing Hands in Action using Discriminative Salient Points and Physics Simulation},
author = {Tzionas, Dimitrios and Ballan, Luca and Srikantha, Abhilash and Aponte, Pablo and Pollefeys, Marc and Gall, Juergen},
journal = {International Journal of Computer Vision (IJCV)},
volume = {118},
number = {2},
pages = {172--193},
month = jun,
year = {2016},
url = {https://doi.org/10.1007/s11263-016-0895-4},
month_numeric = {6} }
This repo was originally developed for the following work, you might be interested in having a look:
@inproceedings{SMPL-X:2019,
title = {Expressive Body Capture: 3D Hands, Face, and Body from a Single Image},
author = {Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.},
booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
year = {2019}
}
Contact
For questions about the code, please contact Vassilis Choutas.