This is the official implementation of our ECCV 2024 paper, "GlobalPointer: Large-Scale Plane Adjustment with Bi-Convex Relaxation". For more details, please visit our project page.

A globally optimal and efficient large-scale plane adjustment, using alternating minimization and convex relaxation techniques.

1. MATLAB r2023a
2. YALMIP Version R20230622
3. MOSEK Version 10.1.11

1. Find our main MATLAB script in main/sythetic_main.mlx and replace the following code.

2. Replace PATH_TO_YALMIP and PATH_TO_MOSEK with the paths to your own YALMIP and MOSEK solvers, respectively.

3. Replace PATH_TO_PROJECT with the path to your project root.

% ---------------------- TODO ----------------------
addpath(genpath("PATH_TO_YALMIP\YALMIP-master"))
addpath(genpath("PATH_TO_MOSEK\Mosek\10.1\toolbox\r2017a"))
root_path = "PATH_TO_PROJECT\GlobalPointer\";
addpath(genpath(root_path))
% ---------------------- TODO END ----------------------

We provide three full experiment setups:

• Increasing point cloud noise
• Increasing pose initialization noise
• Increasing the number of poses and planes

% ---------------------- Experiment Selection Setup ----------------------
% please select your experiment setup
param.increasing_point_noise = false;
param.increasing_pose_noise = false;
param.increasing_scale = true;
% ---------------------- Experiment Selection Setup END ----------------------

These are example results after running the above code. As shown in these figures, we test our method against the classical nonlinear least-squares method, the classical plane adjustment method, and their decoupled variants.

If you find our work useful in your research, please consider citing:

@inproceedings{Liao2024GlobalPointer,
    author 	= {Bangyan Liao and Zhenjun Zhao and Lu Chen and Haoang Li and Daniel Cremers and Peidong Liu},
    title 	= {GlobalPointer: Large-Scale Plane Adjustment with Bi-Convex Relaxation},
    booktitle = {European Conference on Computer Vision (ECCV)},
    year 	= 2024,
    keywords = {Plane Adjustment, Semidefinite Programming (SDP), Convex Relaxation}
}