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Stereo Matching

Stereo Matching is one of the core technologies in computer vision, which recovers 3D structures of real world from 2D images. It has been widely used in areas such as autonomous driving, augmented reality and robotics navigation. Given a pair of rectified stereo images, the goal of Stereo Matching is to compute the disparity for each pixel in the reference image, where disparity is defined as the horizontal displacement between a pair of corresponding pixels in the left and right images.

Source: Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

Papers

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Showing 501–517 of 517 papers

TitleStatusHype
A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images—0
Learning the Matching Function—0
Genetic Stereo Matching Algorithm with Fuzzy Fitness—0
Computing the Stereo Matching Cost with a Convolutional Neural NetworkCode0
Tree-based iterated local search for Markov random fields with applications in image analysis—0
Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling—0
Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching—0
Graph Cut based Continuous Stereo Matching using Locally Shared Labels—0
100+ Times Faster Weighted Median Filter (WMF)—0
Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras—0
Efficient High-Resolution Stereo Matching using Local Plane Sweeps—0
Stereo on a budget—0
Cross-Scale Cost Aggregation for Stereo MatchingCode0
Binary Stereo MatchingCode0
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space—0
Segment-Tree Based Cost Aggregation for Stereo Matching—0
Structural inference affects depth perception in the context of potential occlusion—0
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