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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 301–310 of 517 papers

TitleStatusHype
Tree-based iterated local search for Markov random fields with applications in image analysis—0
TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching—0
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion—0
UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo Matching—0
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems—0
Uncertainty Estimation for End-To-End Learned Dense Stereo Matching via Probabilistic Deep Learning—0
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching—0
Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching—0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching—0
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