Poisson-Based Continuous Surface Generation for Goal-Based Caustics

Overview

This project implements and builds upon the method described in the paper Poisson-Based Continuous Surface Generation for Goal-Based Caustics.

Result simulated with Blender using LuxRender:

Features

Implementation of the Poisson-based continuous surface generation algorithm.
exports a solidified .obj
the only c++17 non-standard dependancy is libpng
can export the the inverse transport map
multithreaded poisson solver

Installation

Prerequisites

C++ compiler with C++17 support for your platform
libpng (requires zlib)

Building from Source: Unix

Clone the repository:

git clone git@github.com:dylanmsu/caustic_engineering.git

Navigate to the project directory:
```
cd caustic_engineering
```

Compile the source code:

# create build directory
mkdir build && cd build

# generate build files
cmake ..

# build
make

Run the code:
```
./CausticEngineering [parameters]
```

Building from Source: Windows

Clone the repository:

git clone git@github.com:dylanmsu/caustic_engineering.git

Navigate to the project directory:
```
cd caustic_engineering
```

Compile the source code:

# create build directory
mkdir build && cd build

# generate build files (assumes C:/mingw64 to be the mingw64 path)
& "C:/mingw64/bin/cmake.exe" -G "MinGW Makefiles" .. 

# build
& "C:/mingw64/bin/mingw32-make.exe"

Run the code:
```
./CausticEngineering.exe [parameters]
```

Usage

Running the Program

Parameters:

  --intput_png=[image path] # input image path

  --res_w=[grid resolution] # mesh resolution in the x-axis

  --width=[mesh width] # physical width of the mesh

  --focal_l=[focal length] # focal length

  --thickness=[lens thickness] # thickness of the final caustic lens

  --max_threads=[max number of threads] # sets the maximum cpu threads for the poisson solver

Execute the program:

./CausticEngineering.exe --intput_png=../img/siggraph.png --res_w=100 --width=0.5 --focal_l=1.5 --thickness=0.1 --max_threads=16

Sample Output

	input_image	parameterization	inverted parameterization	3d_model	simulation
Lena (Mesh: 256 x 256)
Siggraph logo (Mesh: 200 x 200)
Hello World (Mesh: 256 x 128)

transport from an image to an almost uniform distribution:

Prototype

Machined acrylic prototype:

Shadow of the machined prototype:

Future plans

least squares solver for the heightmap -> this will allow more freedom in the lens design
reflective caustics -> is currently not that usefull because of the limited freedom in the height solver
circular caustic lenses
use the fast transport map algoritm from the paper: [https://gnader.github.io/publications/2018-ot-transport.html](Instant Transport Maps on 2D Grids)

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

Fork the repository
Create a new branch (git checkout -b feature)
Make your changes
Commit your changes (git commit -am 'Add new feature')
Push to the branch (git push origin feature)
Create a new Pull Request

License

This project uses the MIT License.

Acknowledgements

Thank you to Yue et al for their awesome research paper.

I would also like to thank Matt Ferraro for providing their informative article about the paper.

dylanmsu/poisson_caustic_design