https://github.com/ybubnov/torch_delaunay
The Delaunay triangulation for PyTorch
https://github.com/ybubnov/torch_delaunay
computational-geometry delaunay-triangulation python triangulation
Last synced: about 1 month ago
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The Delaunay triangulation for PyTorch
- Host: GitHub
- URL: https://github.com/ybubnov/torch_delaunay
- Owner: ybubnov
- License: gpl-3.0
- Created: 2023-01-27T18:10:47.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-28T21:26:55.000Z (2 months ago)
- Last Synced: 2025-04-30T17:04:20.390Z (about 1 month ago)
- Topics: computational-geometry, delaunay-triangulation, python, triangulation
- Language: C++
- Homepage:
- Size: 3.04 MB
- Stars: 18
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# Torch Delaunay - The Delaunay triangulation library for PyTorch
This is a fast library for computing [Delaunay triangulation](https://en.wikipedia.org/wiki/Delaunay_triangulation)
of 2-dimensional points.
The implementation is based on a sweep-algorithm, introduced by David Sinclair[^1] and later
improved by Volodymyr Agafonkin[^2].
Here is an example of triangulation output produced by Torch Delaunay library for a random set of
points:
## Installation
The library is distributed as PyPI package, to install the package, execute the following
command:
```sh
pip install torch_delaunay
```
You can use the `torch_delaunay` library for a fast computation of Delaunay triangulation for
points defined as [PyTorch](https://pytorch.org) tensors.
## Usage
```py
import torch
from torch_delaunay.functional import shull2d
import matplotlib.pyplot as plt
# Compute Delaunay triangulation for randomly-generated 2-dimensional points.
points = torch.rand((100, 2))
simplices = shull2d(points)
# Plot the result
plt.triplot(points[:, 0], points[:, 1], triangles=simplices)
plt.title("Delaunay Triangulation")
```
## Benchmarks
Benchmarks are collected on _Apple M1 Pro_ chip with 32 GiB of memory.
| Prob. Distribution | Data Type | Samples | Time | CPU | Iterations |
| ------------------ | --------- | ------- | -------- | -------- | ---------- |
| Uniform | Float64 | 10000 | 4.18 ms | 4.11 ms | 171 |
| Uniform | Float64 | 100000 | 49.6 ms | 48.3 ms | 15 |
| Uniform | Float64 | 1000000 | 600 ms | 592 ms | 1 |
| Uniform | Float32 | 10000 | 4.37 ms | 4.29 ms | 161 |
| Uniform | Float32 | 100000 | 47.4 ms | 46.4 ms | 15 |
| Uniform | Float32 | 1000000 | 573 ms | 568 ms | 1 |
| Normal | Float64 | 10000 | 4.60 ms | 4.49 ms | 156 |
| Normal | Float64 | 100000 | 48.5 ms | 47.4 ms | 15 |
| Normal | Float64 | 1000000 | 568 ms | 563 ms | 1 |
| Normal | Float32 | 10000 | 4.42 ms | 4.33 ms | 165 |
| Normal | Float32 | 100000 | 46.5 ms | 45.4 ms | 15 |
| Normal | Float32 | 1000000 | 550 ms | 547 ms | 1 |
## License
The Torch Delaunay is distributed under GPLv3 license. See the [LICENSE](LICENSE) file for full
license text.
[^1]: David Sinclair - [S-hull: a fast radial sweep-hull routine for Delaunay triangulation](https://arxiv.org/abs/1604.01428).
[^2]: Volodymyr Agafonkin - [Delaunator: An incredibly fast and robust JavaScript library for Delaunay triangulation of 2D points](https://github.com/mapbox/delaunator).