Real-Time Correlation-Based Stereo Vision with Reduced Border Errors

By Heiko Hirschmüller, Peter R. Innocent, J. Garibaldi. International Journal of Computer Vision, 2002

535 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/14686cbab601311da52323656ed17f05cb03d982

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Citations in the corpus

Evaluation of Cost Functions for Stereo Matching (2007, methodology): “Our local stereo method (Corr) is a simple window-based approach [10, 13, 17].”
A multi-block-matching approach for stereo (2015, background): “Another way of tackling the fattening effect was presented in [19].”
Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue (2012, methodology): “Visual Odometry A correlation-based algorithm [13], [14] is used to obtain a disparity image from two time-synchronized camera images in the stereo-processing component.”
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation (2022, methodology): “Local methods [2, 15, 47] compute matching cost using a support window centered at pixels along the epipolar line.”
A fast area-based stereo matching algorithm (2004, background): “The border-localisation effect, analysed in detail in [16, 12], occurs when the correlation window is placed across regions at different depths.”
Depth from Combining Defocus and Correspondence Using Light-Field Cameras (2013, background): “For correspondence, these areas show large stereo displacement.”
High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth (2014, methodology): “We test our new datasets using 3 state-of-the-art stereo methods that are able to handle high-resolution images: a correlation method employing a 7×7 census transform [33] and aggregation with overlapping 9×9 windows [16]; the fast ELAS method by Geiger et al.”
LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation (2019, methodology): “From matching cost, the peak ratio of the matching costs [11] and naive peak ratio [12] have been widely used to remove unreliable pixels.”
AANet: Adaptive Aggregation Network for Efficient Stereo Matching (2020, methodology): “, using varying window size [26], multiple windows [11], or unconstrained shapes [2].”
Real-time Global Stereo Matching Using Hierarchical Belief Propagation (2006, background): “Local algorithms, which are based on correlation can have very efficient implementation that are suitable for real-time application [17, 18].”
Evaluation of Stereo Matching Costs on Images with Radiometric Differences (2009, unknown): “The filter is often used in local realtime methods [9], [10] À x 2 þ y 2 2 2 e À x 2 þy 2 2 2 :”
Multi-resolution real-time stereo on commodity graphics hardware (2003, methodology): “software [3, 4, 5], there are few CPU cycles left to perform other tasks including high-level interpretation of the ste reo results.”
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Stereo Processing by Semi-global Matching and Mutual Information (background): “Disparity refinement is often done for removing peaks [17], checking the consistency [8], [11], [12], interpolating gaps [17] or increasing the accuracy by sub-pixel interpolation [1], [8].”
Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information (2005, methodology): “The MWMF method is a local, correlation based, real time algorithm [5], which has been shown [8] to produce better object borders (i.”