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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 1–10 of 517 papers

TitleStatusHype
S^2M^2: Scalable Stereo Matching Model for Reliable Depth Estimation—0
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-ExpertsCode2
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather—0
ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo MatchingCode2
StereoDiff: Stereo-Diffusion Synergy for Video Depth Estimation—0
Multi-Label Stereo Matching for Transparent Scene Depth EstimationCode1
M3Depth: Wavelet-Enhanced Depth Estimation on Mars via Mutual Boosting of Dual-Modal Data—0
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World—0
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo MatchingCode0
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