A new approach to the maximum-flow problem
Journal of the ACM (JACM)
A Bayesian approach to binocular stereopsis
International Journal of Computer Vision
Stereo Matching with Nonlinear Diffusion
International Journal of Computer Vision
International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduced-Hessian Quasi-Newton Methods for Unconstrained Optimization
SIAM Journal on Optimization
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Correspondence with Compact Windows via Minimum Ratio Cycle
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Space-Sweep Approach to True Multi-Image Matching
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Stochastic Diffusion for Disparity Estimation
SMBV '01 Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01)
Improvements in Real-Time Correlation-Based Stereo Vision
SMBV '01 Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01)
Automated stereo perception
Efficient graph-based energy minimization methods in computer vision
Efficient graph-based energy minimization methods in computer vision
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Depth Discontinuities by Pixel-to-Pixel Stereo
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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
Stereo Correspondence by Dynamic Programming on a Tree
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming
International Journal of Computer Vision
Combined Depth and Outlier Estimation in Multi-View Stereo
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
High-Quality Real-Time Stereo Using Adaptive Cost Aggregation and Dynamic Programming
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Stereo for Image-Based Rendering using Image Over-Segmentation
International Journal of Computer Vision
Stereo Processing by Semiglobal Matching and Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global Stereo Reconstruction under Second-Order Smoothness Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation-based adaptive support for accurate stereo correspondence
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
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
Fast variable window for stereo correspondence using integral images
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Surfaces with occlusions from layered stereo
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
hSGM: hierarchical pyramid based stereo matching algorithm
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
| Hi-index | 0.00 |
The increasing demand for higher resolution images and higher frame rate videos will always pose a challenge to computational power when real-time performance is required to solve the stereo-matching problem in 3D reconstruction applications. Therefore, the use of asymptotic analysis is necessary to measure the time and space performance of stereo-matching algorithms regardless of the size of the input and of the computational power available. In this paper, we survey several classic stereo-matching algorithms with regard to time---space complexity. We also report running time experiments for several algorithms that are consistent with our complexity analysis. We present a new dense stereo-matching algorithm based on a greedy heuristic path computation in disparity space. A procedure which improves disparity maps in depth discontinuity regions is introduced. This procedure works as a post-processing step for any technique that solves the dense stereo-matching problem. We prove that our algorithm and post-processing procedure have optimal O(n) time---space complexity, where n is the size of a stereo image. Our algorithm performs only a constant number of computations per pixel since it avoids a brute force search over the disparity range. Hence, our algorithm is faster than "real-time" techniques while producing comparable results when evaluated with ground-truth benchmarks. The correctness of our algorithm is demonstrated with experiments in real and synthetic data.