Skip to main content

Probablistic point cloud resitration algorithms

Project description


Build Status Build status PyPI version MIT License Documentation Status Downloads

Probreg is a library that implements point cloud registration algorithms with probablistic model.

The point set registration algorithms using stochastic model are more robust than ICP(Iterative Closest Point). This package implements several algorithms using stochastic models and provides a simple interface with Open3D.

Core features

  • Open3D interface
  • Rigid and non-rigid transformation



type CPD SVR, GMMReg GMMTree FilterReg BCPD (experimental)
Rigid Scale + 6D pose 6D pose 6D pose 6D pose
NonRigid Affine, MCT TPS - Deformable Kinematic
Combined model
(Rigid + Scale + NonRigid-term)

CUDA support

You need to install cupy.

pip install cupy


You can install probreg using pip.

pip install probreg

Or install probreg from source.

git clone --recursive
cd probreg
pip install -e .

Getting Started

This is a sample code that reads a PCD file and calls CPD registration. You can easily execute registrations from Open3D point cloud object and draw the results.

import copy
import numpy as np
import open3d as o3
from probreg import cpd

# load source and target point cloud
source ='bunny.pcd')
target = copy.deepcopy(source)
# transform target point cloud
th = np.deg2rad(30.0)
target.transform(np.array([[np.cos(th), -np.sin(th), 0.0, 0.0],
                           [np.sin(th), np.cos(th), 0.0, 0.0],
                           [0.0, 0.0, 1.0, 0.0],
                           [0.0, 0.0, 0.0, 1.0]]))
source = source.voxel_down_sample(voxel_size=0.005)
target = target.voxel_down_sample(voxel_size=0.005)

# compute cpd registration
tf_param, _, _ = cpd.registration_cpd(source, target)
result = copy.deepcopy(source)
result.points = tf_param.transform(result.points)

# draw result
source.paint_uniform_color([1, 0, 0])
target.paint_uniform_color([0, 1, 0])
result.paint_uniform_color([0, 0, 1])
o3.visualization.draw_geometries([source, target, result])



Compare algorithms

CPD SVR GMMTree FilterReg

Noise test

ICP(Open3D) CPD FilterReg

Non rigid registration

CPD SVR Filterreg BCPD

Feature based registration

FPFH FilterReg

Time measurement

Execute an example script for measuring time.


# Results [s]
# ICP(Open3D):  0.02030642901081592
# CPD:  3.6435861150093842
# SVR:  0.5795929960149806
# GMMTree:  0.34479290700983256
# FilterReg:  0.039795294986106455


    author = {{Kenta-Tanaka et al.}},
    title = {probreg},
    url = {},
    version = {0.1.6},
    date = {2019-9-29},

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for probreg, version 0.3.1
Filename, size File type Python version Upload date Hashes
Filename, size probreg-0.3.1.tar.gz (922.7 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page