Visual-Tactile Sensing for In-Hand Object Reconstruction

This repository contains the implementation of the paper:

Visual-Tactile Sensing for In-Hand Object Reconstruction
Wenqiang Xu*, Zhenjun Yu*, Han Xue, Ruolin Ye, Siqiong Yao, Cewu Lu (* = Equal contribution)
CVPR 2023

If you find our code or paper useful, please consider citing

@inproceedings{xu2023visual,
  title={Visual-Tactile Sensing for In-Hand Object Reconstruction},
  author={Xu, Wenqiang and Yu, Zhenjun and Xue, Han and Ye, Ruolin and Yao, Siqiong and Lu, Cewu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8803--8812},
  year={2023}
}

Get-Started

The code is only tested on Ubuntu, we will soon test it on Windows system.

With conda and pip

Install anaconda or miniconda. Supposing that the name vtaco is used for conda environment:

conda create -y -n vtaco python=3.8
conda activate vtaco

Then, install dependencies with pip install

pip install -r requirements.txt

With Docker

Install Docker under the instructions. Supposing hat the tag vtaco-train is used for docker image:

docker build -t vtaco-train -f ./manifests/Dockerfile .

To start a develop container, run

docker run --ipc=host --rm -it -p 8888:8888 vtaco-train

This will launch a jupyterlab server inside the container. The server can be accessed via port 8888.

If the Docker installation is configured with Nvidia's GPU support, an additional --gpus all flag can be passed

docker run --ipc=host --gpus all --rm -it -p 8888:8888 vtaco-train

To mount the dataset, add an additional --volume mapping.

docker run --ipc=host --gpus all --rm -it -p 8888:8888 --volume <path/to/dataset>:/opt/vtaco/data vtaco-train

Note: The <path/to/dataset> should be replaced by actual path on the host system.

Dataset

We are uploading the dataset, which will be available on https://huggingface.co/datasets/robotflow/vtaco/
You can follow the instructions to download the dataset for training and testing dataset for VTacO and VTacOH.

VT-Sim

The VT-Sim has been released here

Training

To train the Depth Estimator $U_I(\cdot)$ and the sensor pose estimator, we provide a config file configs/tactile/tactile_test.yaml, you can run the following command to train from scratch:

python train_depth.py configs/tactile/tactile_test.yaml

With the pretrained model of $U_I(\cdot)$ and the sensor pose estimator, examples for training VTacO or VTacOH are as follows:

python train.py configs/VTacO/VTacO_YCB.yaml
python train.py configs/VTacOH/VTacOH_YCB.yaml

Note: you might need to change path in data, and model_file in encoder_t2d_kwargs of the config file, to your data path and pretrained model path. This path is out/tactile/test/model_best.pt by default.

All the results will be saved in out/ folder, including checkpoints, visualization results and logs for tensorboard.

jeffsonyu/VTacO