LS-ELAS: Line Segment based Efficient Large Scale Stereo Matching

Abstract

We present LS-ELAS, a line segment extension to the ELAS algorithm, which increases the performance and robustness. LS-ELAS is a binocular dense stereo matching algorithm, which computes the disparities in constant time for most of the pixels in the image and in linear time for a small subset of the pixels (support points). Our approach is based on line segments to determine the support points instead of uniformly selecting them over the image range. This way we find very informative support points which preserve the depth discontinuity. The prior of our Bayesian stereo matching method is based on a set of line segments and a set of support points. Both sets are plugged into a constrained Delaunay triangulation to generate a triangulation mesh which is aware of possible depth discontinuities. We further increased the accuracy by using an adaptive method to sample candidate points along edge segments.

BibTeX

@inproceedings{JellalICRA2017,
  title = {LS-ELAS: Line Segment based Efficient Large Scale Stereo Matching},
  author = {Ait Jellal, Radouane and Lange, Manuel and Wassermann, Benjamin  and Schilling, Andreas and Zell, Andreas},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year = {2017},
  address = {Singapore},
  month = may-jun,
  abstract = {We present LS-ELAS, a line segment extension to the ELAS algorithm, which increases the performance and robustness. LS-ELAS is a binocular dense stereo matching algorithm, which computes the disparities in constant time for most of the pixels in the image and in linear time for a small subset of the pixels (support points). Our approach is based on line segments to determine the support points instead of uniformly selecting them over the image range. This way we find very informative support points which preserve the depth discontinuity. The prior of our Bayesian stereo matching method is based on a set of line segments and a set of support points. Both sets are plugged into a constrained Delaunay triangulation to generate a triangulation mesh which is aware of possible depth discontinuities. We further increased the accuracy by using an adaptive method to sample candidate points along edge segments.},
  days = {29-3},
  pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2017/JellalICRA17.pdf}
}