Publications

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication


Year(s) from:  to 
Author:
Keywords (separated by spaces):

Consensus Maximization with Linear Matrix Inequality Constraints

Pablo Speciale, Danda Pani Paudel, Martin R Oswald, Till Kroeger, Luc Van Gool, and Marc Pollefeys
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
July 2017

Abstract

Consensus maximization has proven to be a useful tool for robust estimation. While randomized methods like RANSAC are fast, they do not guarantee global optimality and fail to manage large amounts of outliers. On the other hand, global methods are commonly slow because they do not exploit the structure of the problem at hand. In this paper, we show that the solution space can be reduced by introducing Linear Matrix Inequality (LMI) constraints. This leads to significant speed ups of the optimization time even for large amounts of outliers, while maintaining global optimality. We study several cases in which the objective variables have a special structure, such as rotation, scaled-rotation, and essential matrices, which are posed as LMI constraints. This is very useful in several standard computer vision problems, such as estimating Similarity Transformations, Absolute Poses, and Relative Poses, for which we obtain compelling results on both synthetic and real datasets. With up to 90 percent outlier rate, where RANSAC often fails, our constrained approach is consistently faster than the non-constrained one - while finding the same global solution.


Link to publisher's page
Download in pdf format
@InProceedings{eth_biwi_01380,
  author = {Pablo Speciale and Danda Pani Paudel and Martin R Oswald and Till Kroeger and Luc Van Gool and and Marc Pollefeys},
  title = {Consensus Maximization with Linear Matrix Inequality Constraints},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
  month = {July},
  keywords = {}
}