Rotation Coordinate Descent (RCD) is a fast rotation averaging algorithm that achieves global optimality under mild noise conditions on the noise level of the measurements.

@inproceedings{parra2021rotation, title={Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging}, author={Parra, Alvaro and Chng, Shin-Fang and Chin, Tat-Jun and Eriksson, Anders and Reid, Ian}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={4298--4307}, year={2021} } 

This demo runs in MATLAB, with RCD and RCDL compiled in C++. We tested under version R2020a on systems with:

• macOS Catalina
• Ubuntu 18.04

Dependencies:

1. CMake 3.0 or later required cmake installation link

   • MacOS
     brew install cmake

   • Ubuntu
     sudo apt-get install cmake

2. SuiteSparse required

   • MacOS brew install suite-sparse

   • Ubuntu sudo apt-get install libsuitesparse-dev

We provide a script build.sh to build RCD and RCDL. Please make sure you have installed all required dependencies (see Section 1). Execute

chmod +x build.sh ./build.sh 

which will create the executables RCD and RCDL in bin folder.

Run demo_rcd.m in MATLAB.

Here, we provide a demonstration on 3 different SfM camera graphs where number of nodes = 1000, 2000 and 3000 and graph density = 0.4. This demo is expected to finish in about 1 minute, which will generate the following outputs:

Run demo_rcdl.m in MATLAB.

Here, we provide a demonstration on 2 different SLAM camera graphs which are torus3D and grid3D. This demo is expected to finish in about 1 minute, which will generate the following outputs: