ubicoders-g2opy 2.1.3

g2o: A General Framework for Graph Optimization (Python bindings)

g2o - General Graph Optimization

Ubicoders g2opy edition

This project is based on the original g2o maintainer as below.

This porject provide windows and linux off the shelf packge including the .dll and .so files as well as the types of the apis for intellisense.

Otherthan that, same as g2o's pymem branch

Latest Commit base:

• 70f058fde4516505ecb3b392b25bd66b2f4fdf47 (Sep 21, 2025)

How it works

Built the .whl with the damn .dll and .so files for god xxxxing sake.

The pip packge name is pyg2o. But import with import g2opy as g2o

Intellisense Enhanced

Supported Python Versions

• 3.10 ✅
• 3.11 ✅
• 3.12 ✅
• 3.13 ✅

Supproted Numpy Version

• numpy 1 ⬜
• numpy 2 ✅

Tested OS and Python

• Windows 11 ✅
• Ubuntu 20 ⬜
• Ubuntu 22 ⬜
• Ubuntu 24 ✅
• Ubuntu 25 ✅

ubuntu dependencies

sudo apt-get install -qq qtdeclarative5-dev qt5-qmake libqglviewer-dev-qt5 libsuitesparse-dev libeigen3-dev -y
pip install "numpy>=2.0"

For the new conda env

conda install -y -c conda-forge "libstdcxx-ng>=12" "libgcc-ng>=12" libgomp

Test the installaton

import numpy as np
import g2opy as g2o
np.set_printoptions(precision=3)
np.set_printoptions(suppress=True)
from collections import defaultdict

def showCameraPoses(optim, idx, N):
    for i in range(idx, idx + N):
        T = optim.vertex(i).estimate().matrix()
        trans = T[:3, 3]
        print('camera pose at ', i, ': ', trans)

def calcSSE(optim, idx, wPts):
    sse = 0
    N = wPts.shape[0]
    for i in range(idx, idx + N):
        guessedWpt = optim.vertex(i)
        error = guessedWpt.estimate() - wPts[i-idx]
        sse += np.sum(error ** 2)
    return sse

def showWpts(optim, idx, N):
    for i in range(idx, idx + N):
        T = optim.vertex(i).estimate()
        #T = np.round(T, 0)
        print('guessed wPt at ', i-idx, ': ', T)

def main():   
    optimizer = g2o.SparseOptimizer()
    solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3())
    solver = g2o.OptimizationAlgorithmLevenberg(solver)
    optimizer.set_algorithm(solver)

    f, p = 200, 256
    baseline = 0.3
    K = np.array([[f, 0, p],
                  [0, f, p],
                  [0, 0, 1]])

    wPts = np.array([
        [0, 0, 10],
        [-1, 3, 30],
        [2, 2, 37.2],
    ])

    num_pose = 10
    for i in range(0, int(num_pose/2)):
        pose = g2o.Isometry3d(np.identity(3), [(i-2)*10, 0, 0])
        v_se3 = g2o.VertexSCam()
        v_se3.set_cam(f, f, p, p, baseline)
        v_se3.set_id(i)
        v_se3.set_estimate(pose)
        if i < 2:
            v_se3.set_fixed(True)
        v_se3.set_all() # sets transfrom, projection, dR (angle)
        optimizer.add_vertex(v_se3)

    for i in range(int(num_pose/2), num_pose):
        pose = g2o.Isometry3d(np.identity(3), [0, (i - int(num_pose/2)- 2) * 10, 0])
        v_se3 = g2o.VertexSCam()
        v_se3.set_cam(f, f, p, p, baseline)
        v_se3.set_id(i)
        v_se3.set_estimate(pose)
        v_se3.set_fixed(False)
        v_se3.set_all() # sets transfrom, projection, dR (angle)
        optimizer.add_vertex(v_se3)

    point_id = 0

    for i, wpt in enumerate(wPts):
        guessed_wPt = g2o.VertexPointXYZ()
        guessed_wPt.set_id(num_pose + point_id)
        guessed_wPt.set_marginalized(True)
        guessed_wPt.set_estimate(wpt + np.random.randn(3) )
        optimizer.add_vertex(guessed_wPt)

        for j in range(num_pose):
            z = optimizer.vertex(j).map_point(wpt)
            if i > 1:
                z +=  np.random.randn(3)
            edge = g2o.EdgeXyzVsc()
            edge.set_vertex(0, guessed_wPt)
            edge.set_vertex(1, optimizer.vertex(j))
            edge.set_measurement(z)
            edge.set_information(np.identity(3))
            optimizer.add_edge(edge)

        point_id += 1

    sse0 = calcSSE(optimizer, num_pose, wPts)
    print('\nRMSE (inliers only):')
    print('before optimization:', np.sqrt(sse0 / wPts.shape[0]))
    showCameraPoses(optimizer, 0, num_pose)
    showWpts(optimizer, num_pose, 3)


    print('Performing full BA:')
    optimizer.initialize_optimization()
    optimizer.set_verbose(False)
    optimizer.optimize(100)

    sse1 = calcSSE(optimizer, num_pose, wPts)

    print('\nRMSE (inliers only):')
    print('after  optimization:', np.sqrt(sse1 / wPts.shape[0]))
    showCameraPoses(optimizer, 0, num_pose)
    showWpts(optimizer, num_pose, 3)


if __name__ == '__main__':
    main()

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see the orignal readme at https://github.com/RainerKuemmerle/g2o