Official implementation of Neural Surface Maps - Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra
Replicating the results is possible following these steps:
- Parametrize the surface
- Prepare surface sample
- Overfit the surface
- Neural parametrization of the surface
- Optimize surface-to-surface map
- Optimize a map between a collection
This is a preprocessing step. You can use SLIM[1] from this repo to fulfill this step.
Given a parametrized surface (prev. step), we need to convert it into a sample. First of all, we need to over sample the surface with Meshlab. You can use the midpoint subdivision filter.
Once the super-sampled surface is ready then you can convert it into a sample:
python -m preprocessing.convert_sample surface_slim.obj surface_slim_oversampled.obj output_sample.pth
The file output_sample.pth
is the sample ready to be over-fitted.
A surface representation is generated with:
python -m training_surface_map dataset.sample_path=output_sample.pth
This will save a surface map inside outputs/neural_maps
folder. The folder name follows this patterns: overfit_[timestamp]
. Inside that folder, the map is saved under the sample
fodler as pth
file.
The overfitted surface can be generated with:
python -m show_surface_map
please, set the path to the pth
file just created inside the script.
Generating a neural parametrization need to run:
python -m training_parametrization_map dataset.sample_path=your_surface_map.pth
Like for the overfitting, this saves the map inside outputs/neural_maps
folder. The folder name have the following patterns parametrization_[timestamp]
.
To display the paramtrization obtained run:
python -m show_parametrization_map
please, set the path to the pth
file just created inside the script.
To generating a inter-surface map run:
python -m training_intersurface_map dataset.sample_path_g=your_surface_map_a.pth dataset.sample_path_f=your_surface_map_b.pth
Note, this steps requires two surface maps. A source, sample_path_g
, and a target, sample_path_f
.
Likewise the overfitting, the map is saved inside outputs/neural_maps
. The inter-surface map folder pattern is intersurface_[timestamp]
. The pth
file is inside the models
folder.
To display the inter-surface map run:
python -m show_intersurface_map
remember to set the path of the maps inside the script.
A collection between a set of surface maps can be optimized with:
python -m training_intersurface_map dataset.sample_path_g=your_surface_map_g.pth dataset.sample_path_f=your_surface_map_f.pth dataset.sample_path_q=your_surface_map_q.pth
Note, this steps requires three surface maps. A source, sample_path_g
, and two targets, sample_path_f
and sample_path_q
.
This will save two maps inside outputs/neural_maps
folder. The folder name follows this patterns: collection_[timestamp]
, under the folder models
you can find two *.pth
file.
To display the collection map run:
python -m show_collection_map
remember to set the path of maps inside the script.
Dependencies are listed in environment.yml
. Using conda, all the packages can be installed with conda env create -f environment.yml
.
On top of the packages above, please install also pytorch svd on gpu package.
Any mesh can be used for this process. A data example can be downloaded here.
@misc{morreale2021neural,
title={Neural Surface Maps},
author={Luca Morreale and Noam Aigerman and Vladimir Kim and Niloy J. Mitra},
year={2021},
eprint={2103.16942},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
[1] Scalable locally injective mappings - Michael Rabinovich et. al. - ACM Transactions on Graphics (TOG) 2017