Many vision applications require high-accuracy dense disparity maps in real-time and online. Due to time constraint, most real-time stereo applications rely on local winner-takes-all optimization in the disparity computation process. These local approaches are generally outperformed by offline global optimization based algorithms. However, recent research shows that, through carefully selecting and aggregating the matching costs of neighboring pixels, the disparity maps produced by a local approach can be more accurate than those generated by many global optimization techniques. We are therefore motivated to investigate whether these cost aggregation approaches can be adopted in real-time stereo applications and, if so, how well they perform under the real-time constraint. The evaluation is conducted on a real-time stereo platform, which utilizes the processing power of programmable graphics hardware. Six recent cost aggregation approaches are implemented and optimized for graphics hardware so that real-time speed can be achieved. The performances of these aggregation approaches in terms of both processing speed and result quality are reported.
|Number of pages||14|
|Journal||International Journal of Computer Vision|
|State||Published - Nov 2007|
Bibliographical noteFunding Information:
This work is supported in part by National Science and Engineering Council of Canada, Laurentian University, University of Kentucky Research Foundation, US Department of Homeland Security, and US National Science Foundation grant IIS-0448185.
- Cost aggregation algorithms
- Programmable graphics hardware
- Real-time stereo matching
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Artificial Intelligence