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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 231–240 of 517 papers

TitleStatusHype
Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity—0
Parameterized Cost Volume for Stereo Matching—0
Image-Coupled Volume Propagation for Stereo Matching—0
Real-Time High-Quality Stereo Matching System on a GPU—0
Efficient stereo matching on embedded GPUs with zero-means cross correlationCode0
Expansion of Visual Hints for Improved Generalization in Stereo Matching—0
Self-Supervised Intensity-Event Stereo Matching—0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map—0
2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation—0
A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images—0
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