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<p align="center"> <h1 align="center"> <ins>GlobalPointer</ins> ⚡️<br>Large-Scale Plane Adjustment with Bi-Convex Relaxation</h1> </p> <h3 align="center">ECCV 2024</h3> <p align="center"> <a href="https://arxiv.org/abs/2407.13537"><img src="https://img.shields.io/badge/arXiv-2407.13537-b31b1b.svg"></a> <a href="https://bangyan101.github.io/GlobalPointer/"><img src="https://img.shields.io/badge/Project-Page-green.svg"/></a> <a href="https://www.youtube.com/watch?v=myE7Epx_5zE"><img src="https://img.shields.io/badge/YouTube-%23FF0000.svg?style=flat&logo=YouTube&logoColor=white" height=22.5></a> </p>

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.

<p align="center"> <a href="https://arxiv.org/abs/2407.13537"> <img src="./media/teaser.png" alt="Logo" width=100%> </a> <br> <em>A globally optimal and efficient large-scale plane adjustment, using alternating minimization and convex relaxation techniques.</em> </p>

Setup

MATLAB r2023a
YALMIP Version R20230622
MOSEK Version 10.1.11

Running

Replace TODO codes

Find our main MATLAB script in main/sythetic_main.mlx and replace the following code.
Replace PATH_TO_YALMIP and PATH_TO_MOSEK with the paths to your own YALMIP and MOSEK solvers, respectively.
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 ----------------------

Select experiment

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 ----------------------

Example results

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.

Citation

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}
}