MeTACAST: Target- and Context-aware Spatial Selection in VR

Paper | Video | MeTACAST user study data and R scripts

MeTACAST is a set of selection techniques for particle data in VR environment. Users can select a group of particles with natural and simple 6DOF point/stroke input. Refer to the Video for a quick review. For more details, please refer to our Paper (MeTACAST: Target- and Context-aware Spatial Selection in VR), which is presented on IEEE Visualization 2023 and published in the journal IEEE Transactions on Visualization and Computer Graphics.
Any pull requests and issues are welcome. If you find it useful, could you please leave a star here? Thanks in advance.

Paper information

L. Zhao, T. Isenberg, F. Xie, H. -N. Liang and L. Yu, "MeTACAST: Target- and Context-Aware Spatial Selection in VR," in IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 1, pp. 480-494, Jan. 2024, doi: 10.1109/TVCG.2023.3326517; open-access versions are available at arXiv.

BibTex

@article{Zhao:2024:MTC,
  author      = {Lixiang Zhao and Tobias Isenberg and Fuqi Xie and Hai-Ning Liang and Lingyun Yu},
  title       = {MeTACAST: Target- and Context-aware Spatial Selection in VR},
  journal     = {IEEE Transactions on Visualization and Computer Graphics},
  year        = {2024},
  volume      = {30},
  number      = {1},
  month       = jan,
  pages       = {480--494},
  doi         = {10.1109/TVCG.2023.3326517},
  shortdoi    = {10/gtnn25},
  doi_url     = {https://doi.org/10.1109/TVCG.2023.3326517},
  oa_hal_url  = {https://hal.science/hal-04196163},
  preprint    = {https://doi.org/10.48550/arXiv.2308.03616},
  osf_url     = {https://osf.io/dvj9n/},
  url         = {https://tobias.isenberg.cc/p/Zhao2024MTC},
  github_url  = {https://github.com/LixiangZhao98/MeTACAST},
  github_url2 = {https://github.com/LixiangZhao98/PointCloud-Visualization-Tool},
  github_url3 = {https://github.com/LixiangZhao98/MeTACAST-study},
  pdf         = {https://tobias.isenberg.cc/personal/papers/Zhao_2024_MTC.pdf},
  video       = {https://youtu.be/R_WRfzgnOAM},
}

Project website

• https://yulingyun.com/MeTACAST/

Related GitHub Repos

PointCloud-Visualization-Tool

How to use

MeTACAST is developed with Unity3D on Windows platform.
Unity version requirement: Version 2020.3.38f1 and 2021.3.19f1 have been tested.
VR headset requirement: HTC Vive Pro/Pro2/Pro eye and Valve Index have been tested.
(Oculus is not supported currently, it wil be added in the future. If you want to use Oculus you need to import Oculus package and revise the input logic.)

Install MeTACAST:

• Clone the repo with git lfs installed or download the archive https://github.com/LixiangZhao98/MeTACAST/archive/refs/heads/master.zip.
• Download Unity Hub and Create a new project. Please refer to sec.1-5 of tutorial if you are new Unity user.
• Install SteamVR Plugin and VIVE Input Utility in this new project. Please refer to sec.7 in tutorial to install packages if you are not familiar with it.
• Set the Stereo Rendering Mode to Multi Pass. In the Unity editor, find Edit/project setting/XR Plug-in Management/OpenVR/Stereo Rendering Mode/Multi Pass.
• Copy the Assets/MeTACAST folder in this repo and place it under the Assets folder of your new project.
• Install Steam on your PC and download SteamVR.
• Connect the VR headset with PC and start SteamVR.
• Back to Unity and play Assets/MeTACAST/Scenes/demo.unity.

Control

• Hold trigger on the right controller to start selection, release trigger to confirm selection
• Use joysticker/touchpad on the right controller to adjust density threshold (MeTABrush and MeTAPaint)
• Hold grip to erase the selected region.
• Hit the undo, redo and reset on the ui (above the data on the left controller) by the red sphere in your right controller to undo, redo and reset
• To switch selection techniques, resolution of the density grid and datasets, click the gameobject script/RunTimeController in Hierarchy and switch them in the inspector window

Project website

https://yulingyun.com/MeTACAST/

User study data and R script

MeTACAST-study
osf

Citations

@article{zhao2023metacast,
  title={MeTACAST: Target-and Context-aware Spatial Selection in VR},
  author={Zhao, Lixiang and Isenberg, Tobias and Xie, Fuqi and Liang, Hai-Ning and Yu, Lingyun},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2023},
  publisher={IEEE}
}

Thanks

Many thanks to the authors of open-source repository: unity-marching-cubes-gpu