https://github.com/harrydobbs/torch_ransac3d
A high-performance implementation of 3D RANSAC (Random Sample Consensus) algorithm using PyTorch and CUDA.
https://github.com/harrydobbs/torch_ransac3d
3d cloud cubiod cuda cylinder plane plane-detection point point-cloud ransac segmentation
Last synced: 5 months ago
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A high-performance implementation of 3D RANSAC (Random Sample Consensus) algorithm using PyTorch and CUDA.
- Host: GitHub
- URL: https://github.com/harrydobbs/torch_ransac3d
- Owner: harrydobbs
- License: mit
- Created: 2024-09-24T00:03:23.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-10-16T22:18:05.000Z (7 months ago)
- Last Synced: 2024-11-22T12:08:00.304Z (5 months ago)
- Topics: 3d, cloud, cubiod, cuda, cylinder, plane, plane-detection, point, point-cloud, ransac, segmentation
- Language: Python
- Homepage:
- Size: 6.61 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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A high-performance implementation of 3D RANSAC algorithm using PyTorch and CUDA.
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## Installation
Requirements: torch, numpy
Install with PyPI :
```
pip install torch-ransac3d
```
## Features
- High-performance RANSAC implementation using PyTorch and CUDA
- Supports fitting of multiple geometric primitives:
- Lines
- Planes
- Spheres
- Batch processing capability for improved efficiency
- Support for both PyTorch tensors and NumPy arrays as input
## Example Usage
### Line Fitting
```python
import torch
import numpy as np
from torch_ransac3d.line import line_fit
# Using PyTorch tensor
points_torch = torch.rand(1000, 3)
direction, point, inliers = line_fit(
pts=points_torch,
thresh=0.01,
max_iterations=1000,
iterations_per_batch=100,
epsilon=1e-8,
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
)
# Using NumPy array
points_numpy = np.random.rand(1000, 3)
direction, point, inliers = line_fit(
pts=points_numpy,
thresh=0.01,
max_iterations=1000,
iterations_per_batch=100,
epsilon=1e-8,
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
)
```
### Plane Fitting
```python
from torch_ransac3d.plane import plane_fit
# Works with both PyTorch tensors and NumPy arrays
points = torch.rand(1000, 3) # or np.random.rand(1000, 3)
equation, inliers = plane_fit(
pts=points,
thresh=0.05,
max_iterations=1000,
iterations_per_batch=100,
epsilon=1e-8,
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
)
```
### Sphere Fitting
```python
from torch_ransac3d.sphere import sphere_fit
# Works with both PyTorch tensors and NumPy arrays
points = torch.rand(1000, 3) # or np.random.rand(1000, 3)
center, radius, inliers = sphere_fit(
pts=points,
thresh=0.05,
max_iterations=1000,
iterations_per_batch=100,
epsilon=1e-8,
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
)
```
## Parameters
- `pts`: Input point cloud (torch.Tensor or numpy.ndarray of shape (N, 3))
- `thresh`: Distance threshold for considering a point as an inlier
- `max_iterations`: Maximum number of RANSAC iterations
- `iterations_per_batch`: Number of iterations to process in parallel
- `epsilon`: Small value to avoid division by zero
- `device`: Torch device to run computations on (CPU or CUDA)
## Input Flexibility
All fitting functions support both PyTorch tensors and NumPy arrays as input. The library automatically converts NumPy arrays to PyTorch tensors internally, allowing for seamless integration with various data formats.
## Batch Processing
All fitting functions support batch processing to improve performance. The `iterations_per_batch` parameter determines how many RANSAC iterations are processed in parallel, leading to significant speedups on GPU hardware.
## Credit
This project is based on the work done at https://github.com/leomariga/pyRANSAC-3D/
## Citation
```
@software{Dobbs_torch_ransac3d,
author = {Dobbs, Harry},
title = {torch\_ransac3d: A high-performance implementation of 3D RANSAC algorithm using PyTorch and CUDA},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/harrydobbs/torch_ransac3d},
}
```
## Contact
**Maintainer:** Harry Dobbs
**Email:** [email protected]