Companion code for E.J. Smith, et al.: 3D Shape Reconstruction from Vision and Touch.

This repository contains a code base and dataset for learning to fuse vision and touch signals from the grasp interaction of a simulated robotic hand and 3D obejct for 3D shape reconstruction. The code comes with pre-defined train/valid/test splits over the dataset released, pretrained models, and training and evaluation scripts. This code base uses a subset of the ABC Dataset (released under MIT License) instead of the dataset listed in the paper due to licensing issues. We appologise for the discrepancy, however, no data could have been released otherwise. We have provided updated reconstruction accuracies for the new dataset below.

If you find this code useful in your research, please consider citing with the following BibTeX entry:

@misc{VisionTouch,
Author = {Edward J. Smith and Roberto Calandra and Adriana Romero and Georgia Gkioxari and David Meger and Jitendra Malik and Michal Drozdzal},
Title = {3D Shape Reconstruction from Vision and Touch},
Year = {2020},
journal = {arXiv:1911.05063},
}

Installation

This code uses PyTorch and PyTorch3D. I recommend you install them both following the install instruction found here.

• Install other dependencies:

$ pip install -r requirements.txt

Dataset

To download the code call the following, keep in mind this will take some time to download and unpack:

$ bash download_data.sh

This is released under a MIT License.

Training

Touch Chart Prediction

To train a model to predict touch charts, ie local geometry at each touch site, first move into the touch chart directory:

$ cd touch_charts

To begin training call:

$ python recon.py --exp_type <exp_type> --exp_id <exp_id> 

where <exp_type> and <exp_id> are the experiment type and id you wish to specify. There are a number of other arguments for changing the default parameters of this training, call with --help to view them.

Checkpoints will be saved under a directory "experiments/checkpoint/<exp_type>/<exp_id>/", specified by --exp_type and --exp_id.

To check training progress with Tensorboard:

$ tensorboard --logdir=experiments/tensorboard/<exp_type>/  --port=6006

The training above will only predict a point cloud for each haptic signal. To optimize a mesh sheet to match this predicted point cloud and produce a predicted touch chart at every touch site call the following:

$ mv data/sheets data/pretrained_sheets
$ python produce_sheets.py.py --save_directory experiments/checkpoint/<exp_type>/<exp_id>/encoder_touch

where <exp_type> and <exp_id> are the same settings as when training. This will first move the premade sheets produced using the pretrained model. If you would like to use the premade sheets simply skip this step. By default the script uses the pretrained model provided to perform this optimization. Regardless of the model used, this will take some time to complete, and if you would like to use slurm to produce these sheets, the sumbit.py file can be called instead.

Global Prediction

To train a model to deform vision charts around touch charts and produce a full surface prediction, first move into the vision chart directory:

$ cd vision_charts

To begin training call:

$ python recon.py --exp_type <exp_type> --exp_id <exp_id> 

where <exp_type> and <exp_id> are the experiment type and id you wish to specify. There are a number of other arguments for changing the default parameters of this training, call with --help to view them.

Checkpoints will be saved under a directory "experiments/checkpoint/<exp_type>/<exp_id>/", specified by --exp_type and --exp_id.

To check training progress with Tensorboard:

$ tensorboard --logdir=experiments/tensorboard/<exp_type>/  --port=6006

The same level of hyperparamter search used in the paper can be reproduced using slurm and the submit.py file located in the same folder.

Evaluation

Touch Chart Prediction

Perform evaluation of the touch chart prediction, from the touch chart directory as follows:

$ python recon.py --eval --exp_type <exp_type> --exp_id <exp_id> 

where <exp_type> and <exp_id> are the experiment type and id specified during training.

Global Prediction

Perform evaluation of the global prediction, from the vision chart directory as follows:

$ python recon.py --eval --exp_type <exp_type> --exp_id <exp_id> 

where <exp_type> and <exp_id> are the experiment type and id specified during training.

Pretrained Models

If you wish to download pretrained models please call the following:

$ bash prepare_models.sh

To produce touch charts using the pretrained model call:

$ cd touch_charts
$ python produce_sheets.py 

As this is a time intensive procedure, if you would like to use slurm to produce these sheets, the sumbit.py file can be called. Premade sheets have also been provided in the dataset however.

To test using the pretrained models to reconstruct objects using different input modalities call:

$ cd vision_charts 
$ python recon.py --pretrained <model> --eval

where <model> is one of either ['empty', 'touch', 'touch_unoccluded', 'touch_occluded', 'unoccluded', 'occluded'].

The following table highlights the reconstruction accuracies of these models on the test set:

	No Input	Touch	Occluded	Unoccluded	Touch + Occluded	Touch + Unoccluded
Chamfer Distance	26.888	6.926	2.936	2.844	2.406	2.468

License

See LICENSE for details.