This package implements the efficient RANSAC algorithm for point clouds. Paper can be found here.
R. Schnabel, R. Wahl, R. Klein "Efficient RANSAC for Point-Cloud Shape Detection", in Computer Graphics Forum, Vol. 26, No. 2, pages 214-226, Blackwell Publishing, June 2007
WARNING: this implementation seems to be incorrect. It is slow and may not produce correct results. I'm sad, but almost certainly I won't revisit this project soon.
The efficient RANSAC algorithm is used to segment and fit primitive shapes (sphere, plane, cylinder, torus, cone) to point clouds. Up to my knowledge, this is the first implementation in Julia.
- easy-to-use primitive recognition
- extensible: it's easy to add new primitive shapes
- fast (work in progress)
- no bitmap
- separate parameters for each shape
- no tori
Install the package by:
] add https://github.com/cserteGT3/RANSAC.jlThe input of the algorithm is a point cloud with associated surface normals. The output is a set of primitive shapes with corresponding sets of points, and the rest of the points that do not belong to any primitives.
Follow the detailed example in the documentation.
Here's an example with a point cloud and the detected primitives colored according to their type.
- The RANSACVisualizer.jl package implements Makie.jl recipes to visualize results from this package.
- There are other RANSAC implementations in Julia, for example:
- ImageProjectiveGeometry.jl: fits planes and lines to 3d points using RANSAC.