A python tool for fitting primitives 3D shapes in point clouds using RANSAC algorithm
pyRANSAC-3D is an open source implementation of Random sample consensus (RANSAC) method. It fits primitive shapes such as planes, cuboids and cylinder in a point cloud to many aplications: 3D slam, 3D reconstruction, object tracking and many others.
Requirements: Numpy
Install with Pypi:
pip3 install pyransac3d
import pyransac3d as pyrsc
points = load_points(.) # Load your point cloud as a numpy array (N, 3)
plane1 = pyrsc.Plane()
best_eq, best_inliers = plane1.fit(points, 0.01)
Results in the plane equation Ax+By+Cz+D:
[0.720, -0.253, 0.646, 1.100]
Loading a noisy sphere's point cloud with r = 5 centered in 0 we can use the following code:
import pyransac3d as pyrsc
points = load_points(.) # Load your point cloud as a numpy array (N, 3)
sph = pyrsc.Sphere()
center, radius, inliers = sph.fit(points, thresh=0.4)
Results:
center: [0.010462385575072288, -0.2855090643954039, 0.02867848979091283]
radius: 5.085218633039647
See CONTRIBUTING
Developed with :heart: by the internet
Mainteiner: Leonardo Mariga
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FAQs
A python tool for fitting primitives 3D shapes in point clouds using RANSAC algorithm
We found that pyransac3d demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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