Cross-Based Local Stereo Matching Using Orthogonal Integral Images

By Ke Zhang, Jiangbo Lu, Gauthier Lafruit. 2009

542 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation (2015, methodology): “To finally estimate the disparity, the authors then perform cross-based cost aggregation [28] and semi-global matching (SGM) [10].”
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches (2015, background): “In cross-based cost aggregation (Zhang et al., 2009) we build a local neighborhood around each location comprising pixels with similar image intensity values with the hope that these pixels belong to the same object.”
Group-Wise Correlation Stereo Network (2019, methodology): “Local methods [37, 34, 20] 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.”
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.”
Intel RealSense Stereoscopic Depth Cameras (2017, unknown): “Examples of published techniques that accomplish this are semiglobal matching [8] and edge-preserving accumulation filters [33, 35].”
On building an accurate stereo matching system on graphics hardware (2011, methodology): “More details about the method can be found in [23].”
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.”
Computing the stereo matching cost with a convolutional neural network (2014, background): “[21] suggest that aggregation should consider the support regions of both images in a stereo pair.”
Fast cost-volume filtering for visual correspondence and beyond (2011, methodology): “[19] also proposed an O(1) method for local stereo matching.”