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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 371–380 of 517 papers

TitleStatusHype
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching—0
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World—0
Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching—0
Category-level Object Detection, Pose Estimation and Reconstruction from Stereo Images—0
CATS: A Color and Thermal Stereo Benchmark—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map—0
Confidence Inference for Focused Learning in Stereo Matching—0
Consistency-aware Self-Training for Iterative-based Stereo Matching—0
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