probreg
- Software
- Perception
- Library
A Python library implementing probabilistic point cloud registration algorithms, offering a simple interface with Open3D. It is presented as more robust than ICP.
Source on GitHub: github.com/neka-nat/probreg (opens in a new tab). Facts read at commit 6ffd596 (opens in a new tab).
Licenses
- Code
- MIT (opens in a new tab)
Read by Robotheca from LICENSE (opens in a new tab) (MIT) at commit 6ffd596 (opens in a new tab), on 22 Oct 2026.
- Commercial use
- Yes
What it does
- Implements several stochastic-model registration methods: CPD, Extended CPD, Color CPD, FilterReg, BCPD, GMMReg, Support Vector Registration and GMMTree.
- Supports rigid and non-rigid transformations, including scale, affine, TPS and deformable kinematic models.
- Works directly on Open3D point cloud objects and can draw registration results.
- Optional CUDA support through cupy for rigid and affine CPD.
- Installable with pip or from source.
Written by Robotheca from the project's README (opens in a new tab) at commit 6ffd596.
Getting started
- Difficulty
- Intermediate
- How to start
- The project's README (opens in a new tab)
Maintenance
- Status
- Maintained: a commit in the last 12 months
- Newest commit
- 21 Jul 2026 on master
- Latest release
- None published on GitHub
Checked on GitHub on 22 Oct 2026.
Links
Robots that can run it
No robot on Robotheca is linked to it yet. Links come from a robot's robot.yaml, from its dependency files read as text, or from Robotheca's editors.
Robotheca lists software as a reference: nothing is hosted or run here, and every fact links to where it was read.