ROBA: Rotation-Only Bundle Adjustment

In this repository, we provide the implementation of ROBA. If you use our code, please cite it as follows:

@InProceedings{Lee_2021_CVPR,
author = {Lee, Seong Hun and Civera, Javier},
title = {Rotation-Only Bundle Adjustment},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}
}

Quick start

To initialize the rotations, ROBA relies on rotation averaging. Specifically, we used the rotation averaging code provided by Chatterjee and Govindu. Go to their webpage and download their code (PAMI version). Extract SO3GraphAveraging folder and save it in the same directory.
Download the processed input/results data from here and save it in the same directory.
In Matlab, run the following command to compile the mex function: mex computeEdgeCost.cpp.
Run RunSynthetic.m (or RunReal.m) to try it on the synthetic (or real) data.

File descriptions

RunSynthetic.m: Run the experiment on synthetic data. Set draw3D = true to visualize the ground truth data. Choose the simulation configuration by setting the config variable.
RunReal.m: Run the experiment on the real data. Set plotResult = true to show the accuracy evolution over iterations. Set datasets to select the datasets to evaluate. The Booleans square_rooting, switch_alpha and approximate_gradient are for the ablation study in the paper.
computeEdgeCost.cpp: C++ Mex implementation of Algorithm 1 in our paper.
GetGroundTruthRealData.m: Parse data from 1DSfM datasets. It is not necessary to run this script, as we already provide the parsed data here. If you want to try this yourself, then you need to download the raw data (.out, coords.txt, EGs.txt, tracks.txt) from their webpage.
LoadAndPlotResultsInPaper.m: Load and plot the published results in our paper. Our results are available here.