A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Real-Time Correlation-Based Stereo Vision with Reduced Border Errors
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stereo Machine for Video-Rate Dense Depth Mapping and Its New Applications
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
An Energy Minimisation Approach to Stereo-Temporal Dense Reconstruction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Real-Time Stereo by using Dynamic Programming
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 3 - Volume 03
Improved Real-Time Stereo on Commodity Graphics Hardware
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 3 - Volume 03
Spacetime Stereo: A Unifying Framework for Depth from Triangulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image-Gradient-Guided Real-Time Stereo on Graphics Hardware
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Near Real-Time Reliable Stereo Matching Using Programmable Graphics Hardware
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Symmetric Stereo Matching for Occlusion Handling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multi-resolution real-time stereo on commodity graphics hardware
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Real-Time Neighborhood Based Disparity Estimation Incorporating Temporal Evidence
Proceedings of the 30th DAGM symposium on Pattern Recognition
Real-time joint disparity and disparity flow estimation on programmable graphics hardware
Computer Vision and Image Understanding
Phase-Correlation Guided Search for Realtime Stereo Vision
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Temporal consistent real-time stereo for intelligent vehicles
Pattern Recognition Letters
Tuning stereo image matching with stereo video sequence processing
Proceedings of the 2012 Joint International Conference on Human-Centered Computer Environments
| Hi-index | 0.01 |
Real-time stereo matching has many important applications in areas such as robotic navigation and immersive teleconferencing. When processing stereo sequences most existing real-time stereo algorithms calculate disparity maps for different frames independently without considering temporal consistency between adjacent frames. While it is known that temporal consistency information can help to produce better results, there is no efficient way to enforce temporal consistency in real-time applications. In this paper the temporal correspondences between disparity maps of adjacent frames are modeled using a new concept called disparity flow. A disparity flow map for a given view depicts the 3D motion in the scene that is observed from this view. An algorithm is developed to compute both disparity maps and disparity flow maps in an integrated process. The disparity flow map generated for the current frame is used to predict the disparity map for the next frame and hence, the temporal consistency between the two frames is enforced. All computations are performed in the image space of the given view, leading to an efficient implementation. In addition, most calculations are executed on programmable graphics hardware which further accelerates the processing speed. The current implementation can achieve 89 million disparity estimations per second on an ATI Radeon X800 graphic card. Experimental results on two stereo sequences demonstrate the effectiveness of the algorithm.