/Rotation_Coordinate_Descent

(CVPR 2020 Oral) A fast global rotation averaging algorithm.

Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging

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.

Citation

@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}
}

Quick Start

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

Setup

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

Running demo

Building RCD and RCDL libraries

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.

Running RCD

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:

Running RCDL

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: