A non-local cost aggregation method for stereo matching

By Qingxiong Yang. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

433 citations recorded by the source corpus.

How researchers see this paper

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GA-Net: Guided Aggregation Net for End-To-End Stereo Matching (2019, methodology): “proposed to aggregate costs using a minimum spanning tree [28].”
Recurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference (2019, methodology): “To improve, cost aggregation methods [28, 31] filter the matching cost C(d) at different depths (Fig.”
Group-Wise Correlation Stereo Network (2019, methodology): “Local meth-ods [30, 27, 15] explore different strategies to aggregate matching costs with neighbor pixels and usually utilize the winner-take-all (WTA) strategy to choose the disparity with minimum matching cost.”
Stereo Video Object Segmentation Using Stereoscopic Foreground Trajectories (2019, methodology): “Recently, a few methods [28]–[30] attempted to refine the disparity map with more computational costs.”
Constant Time Weighted Median Filtering for Stereo Matching and Beyond (2013, background): “As a byproduct, the fast weighted median filtering unleashes its potential in other applications that were hampered by high complexities.”
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching (2019, methodology): “Local methods [24, 42, 49] aggregate matching costs with neighboring pixels and usually utilize the winner-take-all strategy to choose the optimal disparity.”