Stereo matching has been one of the most active research topics in computer vision domain for many years resulting in a large number of techniques proposed in the literature. Nevertheless, improper combinations of available tools cannot fully utilize the advantages of each method and may even lower the performance of the stereo matching system. Moreover, state-of-the-art techniques are usually optimized to perform well on a certain input dataset. In this paper we propose a framework to combine existing tools into a stereo matching pipeline and three different architectures combining existing processing steps to build stereo matching systems which are not only accurate but also efficient and robust under different operating conditions. Thorough experiments on three well-known datasets confirm the effectiveness of our proposed systems on any input data.
Nguyen, MD, Hanca, JT, Lu, S-P & Munteanu, A 2015, Robust stereo matching using Census cost, discontinuity-preserving disparity computation and view-consistent refinement. in IEEE International Conference on3D Imaging, IC3D . IEEE, pp. 1-8, 2015 International Conference on 3D Imaging (IC3D), Liege, Belgium, 14/12/15.
Nguyen, M. D., Hanca, J. T., Lu, S.-P., & Munteanu, A. (2015). Robust stereo matching using Census cost, discontinuity-preserving disparity computation and view-consistent refinement. In IEEE International Conference on3D Imaging, IC3D (pp. 1-8). IEEE.
@inproceedings{06f053067224473ca3a36d7b863a65ad,
title = "Robust stereo matching using Census cost, discontinuity-preserving disparity computation and view-consistent refinement",
abstract = "Stereo matching has been one of the most active research topics in computer vision domain for many years resulting in a large number of techniques proposed in the literature. Nevertheless, improper combinations of available tools cannot fully utilize the advantages of each method and may even lower the performance of the stereo matching system. Moreover, state-of-the-art techniques are usually optimized to perform well on a certain input dataset. In this paper we propose a framework to combine existing tools into a stereo matching pipeline and three different architectures combining existing processing steps to build stereo matching systems which are not only accurate but also efficient and robust under different operating conditions. Thorough experiments on three well-known datasets confirm the effectiveness of our proposed systems on any input data.",
keywords = "Stereo matching, depth estimation, disparity estimation, binocular stereo",
author = "Nguyen, {Minh Duc} and Hanca, {Jan Tomasz} and Shao-Ping Lu and Adrian Munteanu",
year = "2015",
month = dec,
day = "14",
language = "English",
isbn = "978-1-5090-1264-0",
pages = "1--8",
booktitle = "IEEE International Conference on3D Imaging, IC3D",
publisher = "IEEE",
note = "2015 International Conference on 3D Imaging (IC3D) ; Conference date: 14-12-2015 Through 15-12-2015",
}