@inproceedings{9ad3b1c59a644a36b6eec7eba20ef168,
title = "Variational model for depth estimation from images",
abstract = "We propose a variant of convex reformulation of the standard variational model with non-convex data terms. The proposed convex relaxation of multilabel problems is a continuous formulation of Ishikawa's method for extension of the graph min-cut to the multilabel problems. Our convex continuous reformulation is based upon functional lifting to a higher-dimensional space using superlevel functions. We solve the resulting convex variational problem by the augmented Lagrangian method. The most time consuming part of this method is the numerical solution of a boundary value problem for the Poisson equation in three-dimensional space, which is implemented by means of a fast Poisson solver. We illustrate the developed theory with several numerical examples for the standard correspondence problem for a rectified stereo image pair.",
keywords = "Computer vision, Minimization methods, Optimization",
author = "Alexander Malyshev and Xue-Cheng TAI",
note = "Funding Information: Supported by the Research Executive Agency of the European Commission, grant 778035 - PDE-GIR - H2020-MSCA-RISE-2017.; 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019 ; Conference date: 26-08-2019 Through 28-08-2019",
year = "2019",
month = aug,
doi = "10.1109/SKIMA47702.2019.8982425",
language = "English",
series = "2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019",
publisher = "IEEE",
booktitle = "2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019",
address = "United States",
}