https://github.com/lyd405121/PBD

Using taichi to implement position based dynamic
https://github.com/lyd405121/PBD

Last synced: 8 days ago
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Using taichi to implement position based dynamic

• Host: GitHub
• URL: https://github.com/lyd405121/PBD
• Owner: lyd405121
• Created: 2022-05-10T07:58:59.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-06-04T03:11:21.000Z (over 2 years ago)
• Last Synced: 2024-08-02T11:23:38.888Z (3 months ago)
• Language: Python
• Size: 6.7 MB
• Stars: 15
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-taichi - Positon based dynamic with Taichi - Positon-based dynamics with Taichi. (Applications / **Simulation**)
• awesome-taichi - Positon based dynamic with Taichi - Positon based dynamic with Taichi. (**Simulation**)

README

        
# Positon based dynamic with taichi

- The code implements one of nvidia's paper [PBD](https://mmacklin.com/uppfrta_preprint.pdf)

- The code uses [Taichi](https://github.com/taichi-dev/taichi) programming language

---

## How to run 

- First config your anaconda workspace, and open the anaconda prompt

  

- Second you need to install dependency  

  ```python

  pip install -r requirements.txt

  ```

- Last you type 

  ```python

  ti Example.py

  ```

---

## How to generate voxel file

- First copy your obj file to "model" , for example "Bearings.obj"

  

- Second you need to modify the code in GenVoxel.py

  ```python

  obj_name = "Bearings"

  ```

- Then you type 

  ```python

  ti GenVoxel.py

  ```

- Last you will get a voxel file like below:

![image](image/howto.png)

## Some example

- Rigidbody Simulation

- Red particle means it contact ground plane, green means it has at least one neighbour from other rigidbody 

![image](image/taichi.gif)