https://github.com/dcharatan/point2mesh-reimplementation
A reimplementation of Hanocka et al.'s paper Point2Mesh: A Self-Prior for Deformable Meshes for CSCI 1470: Deep Learning at Brown University.
https://github.com/dcharatan/point2mesh-reimplementation
graphics mesh-processing meshes point2mesh reimplementation tensorflow
Last synced: 25 days ago
JSON representation
A reimplementation of Hanocka et al.'s paper Point2Mesh: A Self-Prior for Deformable Meshes for CSCI 1470: Deep Learning at Brown University.
- Host: GitHub
- URL: https://github.com/dcharatan/point2mesh-reimplementation
- Owner: dcharatan
- Created: 2020-11-13T18:19:34.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2020-12-16T20:46:40.000Z (over 5 years ago)
- Last Synced: 2025-05-06T06:46:02.124Z (11 months ago)
- Topics: graphics, mesh-processing, meshes, point2mesh, reimplementation, tensorflow
- Language: Python
- Homepage:
- Size: 7.35 MB
- Stars: 38
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Point2Mesh Reimplementation
This is a reimplementation of [Point2Mesh: A Self-Prior for Deformable Meshes](https://ranahanocka.github.io/point2mesh/) by Rana Hanocka, Gal Metzer, Raja Giryes and Daniel Cohen-Or. It was written by David Charatan, Solon James and Grace Deng as a final project for CSCI 1470: Deep Learning at Brown University. The authors' original implementation (in PyTorch) can be found [here](https://github.com/ranahanocka/Point2Mesh/). Watch a short video we made about the project [here](https://youtu.be/k34zExyJHvo).
## Running the Code
Several example point clouds can be found in the `data` folder. To fit to a point cloud, do the following:
```
python3 -m source.script_modules.train_model data/point_clouds/elephant_settings.json
```
Replace `elephant_settings.json` with a JSON settings file of your choice. For more information about valid JSON settings files, see `options.py`.
## Notes About this Implementation
This implementation runs slower than the original implementation and doesn't include the PartMesh structure (splitting meshes into parts so that larger meshes can be optimized). However, we tried to add lots of comments to the code to make this implementation easier to follow than the original.
## Project Setup
### Creating a Virtual Environment
To create a virtual environment, run `python3 -m venv venv`. Then, do `source venv/bin/activate` (or equivalent) and `pip3 install -r requirements.txt` to install the project's dependencies.
### Manifold Software Dependency
The remeshing and simplification operations Point2Mesh depends on require [this watertight manifold software](https://github.com/hjwdzh/Manifold). To install it, `cd` into the `point2mesh-reimplementation` folder and run the following:
```
git clone --recursive -j8 git://github.com/hjwdzh/Manifold
cd Manifold
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make
```
### Installing OpenEXR
OpenEXR, a dependency of TensorFlow Graphics, cannot be installed directly via `pip`. Instead, follow the steps below:
#### Windows
Download a precompiled wheel file from `https://www.lfd.uci.edu/~gohlke/pythonlibs/`, move it to the workspace folder, then run `python -m pip install SomePackage-1.0-py2.py3-none-any.whl`. Running `pip3 install tensorflow-graphics` should then work.
#### MacOS
```
brew install openexr
export CFLAGS="-I/Users/USERNAME/homebrew/include/OpenEXR -std=c++11"
export LDFLAGS="-L/Users/USERNAME/homebrew/lib"
pip3 install tensorflow-graphics
```
#### Ubuntu
```
sudo apt-get install python3-dev
sudo apt-get install libopenexr-dev
sudo apt-get install openexr
pip3 install tensorflow-graphics
```
### Running Scripts
The scripts in `script_modules` can be run as Python modules. To run `create_convex_hull.py`, run the command `python3 -m source.script_modules.create_convex_hull` from the project's root directory. Several VS Code run configurations are included in `.vscode/launch.json`.
### Running Unit Tests
To run the unit tests in VS Code, open the command menu (⌘ Command ⇧ Shift P on MacOS) and run `Python: Discover Tests` and then `Python: Run All Tests`. I recommend discovering and running tests through Python Test Explorer for Visual Studio Code (`littlefoxteam.vscode-python-test-adapter`) if you add more tests, since it's hard to diagnose broken tests that aren't being discovered with the default test explorer.
### GCP VM Creation Steps
Disclaimer: This is a bit hacky since GCP's setup precludes usage of the normal venv setup.
First, make a VM:
- Region: `us-west1 (Oregon)`
- Series: `N1`
- Machine type: `n1-standard-4 (4 vCPU, 15 GB memory)`
- Under `CPU platform and GPU`: Add one `NVIDIA Tesla V100` GPU
- Scroll down and check `Allow HTTP traffic` and `Allow HTTPS traffic`
- Set the boot disk to `Deep Learning on Linux` and `Intel® optimized Deep Learning Image: TensorFlow 2.3 m59 (with Intel® MKL-DNN/MKL and CUDA 110)`.
Now, SSH into the VM under `Open in browser window`. You might have to wait a few minutes before this is possible (before that, it might hang). Now, in the terminal:
- When it prompts you for driver installation when you first log in, say yes.
Now run the following commands:
```
git clone https://github.com/dcharatan/point2mesh-reimplementation.git
cd point2mesh-reimplementation
git clone --recursive -j8 git://github.com/hjwdzh/Manifold
cd Manifold
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make
cd ~/point2mesh-reimplementation
pip3 install trimesh
rm -rf /opt/conda/lib/python3.7/site-packages/cloudpickle
pip3 install cloudpickle==1.4.0
sudo apt-get install libopenexr-dev
sudo apt-get install openexr
pip3 install tensorflow-graphics
```
Now you can run the training script via:
```
python3 -m source.script_modules.train_model
```
### Retrieving OBJs from Google Cloud
```
mv results ~
zip -r temp.zip results
```
If using the browser-based SSH terminal, now click the cog (settings icon) and download `temp.zip`.
## Acknowledgements
The files `elephant.pwn`, `hand.pwn` and `sphere.pwn` were taken from Alex Jacobson's [mesh reconstruction project](https://github.com/alecjacobson/geometry-processing-mesh-reconstruction).