ML Anthology

Dense Disparity Maps from Sparse Disparity Measurements

Simon Hawe, Martin Kleinsteuber, Klaus Diepold
ICCV 2011 pp. 2126-2133
doi:10.1109/ICCV.2011.6126488 /iccv/2011/hawe2011iccv-dense/

Abstract

In this work we propose a method for estimating disparity maps from very few measurements. Based on the theory of Compressive Sensing, our algorithm accurately reconstructs disparity maps only using about 5% of the entire map. We propose a conjugate subgradient method for the arising optimization problem that is applicable to large scale systems and recovers the disparity map efficiently. Experiments are provided that show the effectiveness of the proposed approach and robust behavior under noisy conditions.

PDF ICCV Semantic Scholar

Cite

Text

Hawe et al. "Dense Disparity Maps from Sparse Disparity Measurements." IEEE/CVF International Conference on Computer Vision, 2011. doi:10.1109/ICCV.2011.6126488

Markdown

[Hawe et al. "Dense Disparity Maps from Sparse Disparity Measurements." IEEE/CVF International Conference on Computer Vision, 2011.](https://mlanthology.org/iccv/2011/hawe2011iccv-dense/) doi:10.1109/ICCV.2011.6126488

BibTeX

@inproceedings{hawe2011iccv-dense,
  title     = {{Dense Disparity Maps from Sparse Disparity Measurements}},
  author    = {Hawe, Simon and Kleinsteuber, Martin and Diepold, Klaus},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2011},
  pages     = {2126-2133},
  doi       = {10.1109/ICCV.2011.6126488},
  url       = {https://mlanthology.org/iccv/2011/hawe2011iccv-dense/}
}