Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases [Papers 1, 2][Project page] [Video]

The implementation of the papers

• Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases (ICCV'21)

  Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua, Noam Aigerman

• Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases (preprint)

  Jan Bednarik, Noam Aigerman, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua

Install

The framework was tested with Python 3.8, PyTorch 1.7.0. and CUDA 11.0. The easiest way to work with the code is to create a new virtual Python environment and install the required packages.

1. Install the virtualenvwrapper.
2. Create a new environment and install the required packages.

mkvirtualenv --python=python3.8 tcsr
pip install -r requirements.txt

3. Install Pytorch3d.

cd ~
curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
tar xzf 1.10.0.tar.gz
export CUB_HOME=$PWD/cub-1.10.0
pip install git+https://github.com/facebookresearch/pytorch3d.git@3c15a6c2469249c8b90a4f3e41e34350b8051b92

4. Get the code and prepare the environment as follows:

git clone [email protected]:bednarikjan/temporally_coherent_surface_reconstruction.git
git submodule update --init --recursive
export PYTHONPATH="{PYTHONPATH}:path/to/dir/temporally_coherent_surface_reconstruction"

Get the Data

The project was tested on 6 base datasets (and their derivatives). Each datasets has to be processed so as to generate the input point clouds for training, the GT correspondences for evauluation and other auxilliary data. To do so, please use the individual scripts in tcsr/process_datasets. For each dataset, follow these steps:

1. Download the data (links below).
2. Open the script <dataset_name>.py and set the input/output paths.
3. Run the script: python <dataset_name>.py

1. ANIM

• Download the sequences horse gallop, horse collapse, camel gallop, camel collapse, and elephant gallop.
• Download the sequence walking cat.

2. AMA

• Download all 10 sequences, meshes only.

3. DFAUST

• Download the whole datset.

4. CAPE

• Request the access to the raw scans and download it.
• At the time of writing the paper (September 2021) four subjects (00032, 00096, 00159, 03223) were available and used in the paper.

5. INRIA

• Request the access to the dataset and download it.
• At the time of writing the paper (September 2021), four subjects (s1, s2, s3, s6) were available and used in the paper.

6. CMU

• Download the sequence 171026_pose3 using the PanopticStudio Toolbox.
• Generate the point clouds from the Kinect recordings using KinopticStudio Toolbox (requires Matlab).

Train

The provided code allows for training our proposed method (OUR) but also the other atlas based approaches Differential Surface Representation (DSR) and AtlasNet (AN). The training is configured using the *.yaml configuration scripts in tcsr/train/configs.

There are 9 sample configuration files our_<dataset_name>.yaml which train OUR on each individual dataset and 2 sample configuration files an_anim.yaml, dsr_anim.yaml which train AN and DSR respectivelly on ANIM dataset.

By default, the trainin uses the exact settings as in the paper, namely it trains for 200'000 iterations using SGD, learning rate of 0.001 and batch size of 4. This can be altered in the configuration files.

Before starting the training, follow these steps:

• Open the source file tcsr/data/data_loader.py and set the paths to the datasets in each dataset class.
• Open the desired training configuration *.yaml file in tcsr/train/configs/ and set the output path for the training run data in the attribute path_train_run.

Start the training usint the script tcsr/train/train.py:

python train.py --conf configs/<file_name>.yaml

By default the script saves the training progress each 2000 iterations so you can safely kill it at any point and resume the trianing later using:

python train.py --cont path/to/training_run/root_dir

Evaluate

To evaluate a trianed model on the dense correspondence prediction task, use the script tcsr/evaluate/eval_dataset.py which allows for evaluation of multiple sequences (i.e. individual training runs within one dataset) at once. Please have a look at the command line arguments in the file.

An example of how to run the evaluation for the training runs contained in the root directory train_runs_root corresponding to 2 training runs run for the sequences cat_walk and horse_gallop within ANIM dataset:

python eval_dataset.py /path/to/train_runs_root --ds anim --include_seqs cat_walk horse_gallop  

The script produces a *.csv file in train_runs_root with the 4 measured metrics (see the paper).

Visualize

There are currently two ways to visualize the predictions.

1. Tensorboard

By default, the training script saves the GT and the predicted point clouds (for a couple of random data samples) each 2000 iterations. These can be viewed within Tensorboard. Each patch is visualized with a different color. This visualization is mostly useful as a sanity check during the trianing to see that the model is converging as expected.

• Navigate to the root directory of the trianing runs and run:

tensorboard --logdir=. --port=8008 --bind_all

• Open your browser and navigate to http://localhost:8008/

2. Per-sequence reconstruction GIF

You can view the reconstructed surfaces as a patch-wise textured mesh as a video within a GIF file. For this purpose, use the IPython Notebook file tcsr/visualize/render_uv.ipynb and open it in jupyterlab which allows for viewing the GIF right after running the code.

The rendering parameters (such as the camera location, texturing mode, gif speed etc.) are set usin the configuration file tcsr/visualize/conf_patches.yaml. There are sample configurations for the sequence cat_walk, which can be used to write configurations for other sequences/datasets.

Before running the cells, set the variables in the second cell (paths, models, data).

Citation

@inproceedings{bednarik2021temporally_coherent,
   title = {Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases},
   author = {Bednarik, Jan and Kim, Vladimir G. and Chaudhuri, Siddhartha and Parashar, Shaifali and Salzmann, Mathieu and Fua, Pascal and Aigerman, Noam},
   booktitle = {Proceedings of IEEE International Conference on Computer Vision (ICCV)},
   year = {2021}
}

@inproceedings{bednarik2021temporally_consistent,
   title = {Temporally-Consistent Surface Reconstruction via Metrically-Consistent Atlases},
   author = {Bednarik, Jan and Aigerman, Noam and Kim, Vladimir G. and Chaudhuri, Siddhartha and Parashar, Shaifali and Salzmann, Mathieu and Fua, Pascal},
   booktitle = {arXiv},
   year = {2021}
}

Acknowledgements

This work was partially done while the main author was an intern at Adobe Research.

TODO

• Add support for visualizing the correspondence error heatmap on the GT mesh.
• Add support for visualizing the colorcoded correspondences on the GT mesh.
• Add the support for generating the pre-aligned AMAa dataset using ICP.
• Add the code for the nonrigid ICP experiments.