Globally-Optimal Spherical Mixture Alignment (GOSMA)

This code implements the GOSMA algorithm for estimating the position and orientation of a calibrated camera from a single image with respect to a 3D model.

Installing

Dependencies

Eigen: instructions for downloading and installing can be found at http://eigen.tuxfamily.org/.
CppOptimizationLibrary: instructions for downloading can be found at https://github.com/PatWie/CppNumericalSolvers
CUDA: instructions for downloading and installing can be found at https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html

Operating System

Developed and tested on Ubuntu 18.04 LTS
May be compiled on other systems, although no guarantee is given

Installing on Linux

In the src folder, copy (or link to) the CppNumericalSolvers folder
In the release folder, open a terminal
Run: cmake -D CMAKE_BUILD_TYPE=Release ../src
Run: make

Running

Run the following command from the terminal in the Release folder (arguments in square brackets are optional):

./gosma point-set_filename bearing-vector-set_filename config_filename output_filename [translation_domain_filename [rotation_domain_filename]]

Inputs

point-set_filename: filename of a plain text file with rows of whitespace-separated coordinates [x y z]
bearing-vector-set_filename: filename of a plain text file with rows of whitespace-separated normalised coordinates [x y z]
config_filename: filename of a configuration file, see ./release/config.txt
output_filename: filename of the file in which to append the program output
translation_domain_filename [optional]: filename of a plain text file with rows of whitespace-separated coordinates and side-widths [x y z wx wy wz] where [x y z] are the coordinates of minimum vertex and [wx wy wz] are the cuboid's side-widths
rotation_domain_filename [optional]: filename of a plain text file with rows of whitespace-separated coordinates and side-widths [x y z wx wy wz] where [x y z] are the coordinates of minimum vertex and [wx wy wz] are the cuboid's side-widths
If more than a single class is present, the user may optionally include class count files, to be located in the same directory as the data files (point-set and bearing vector set files).
- Assumes that the file name is the same as the data file name with "_class_counts" appended
  - e.g. point_set_class_counts.txt
- Format: <label number>
  - Class labels are assumed to correspond between 2D and 3D data

Output

The output is appended to the output file in the form [tx ty tz R11 R12 R13 R21 R22 R23 R31 R32 R33 l d1 d2 c]
t is a 3x1 translation vector
R is a 3x3 rotation matrix such that the transformed point-set P' = R(P - t) is aligned with the bearing vector set F
l is L2 distance of the mixture models at the output transformation
d1 is the runtime duration (in seconds) of the GOSMA algorithm
d2 is the runtime duration (in seconds) of generating the mixture models
c is the optimality certificate (1 if optimal)

Settings

See ./release/config.txt
GPUs: if more than one GPU is available, adjust kNumDevices in gosma.cu accordingly

Citation

Any publications resulting from the use of this code should cite the following paper:

Dylan Campbell, Lars Petersson, Laurent Kneip, Hongdong Li, and Stephen Gould, "The Alignment of the Spheres: Globally-Optimal Spherical Mixture Alignment for Camera Pose Estimation", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, IEEE, Jun. 2019

Please use the following bibtex entry:

@inproceedings{campbell2019globally,
  author = {Campbell, Dylan and Petersson, Lars and Kneip, Laurent and Li, Hongdong and Gould, Stephen},
  title = {The Alignment of the Spheres: Globally-Optimal Spherical Mixture Alignment for Camera Pose Estimation},
  booktitle = {Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={to appear},
  address={Long Beach, USA},
  month = {June},
  year = {2019},
  doi={},
  organization={IEEE}
}

Licence

This project is licensed under the GNU General Public License v3.0 - see the LICENSE.md file for details.

AzureSilent/gosma