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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

Showing 3140 of 517 papers

TitleStatusHype
Uncertainty Quantification in Stereo MatchingCode0
MobiFuse: A High-Precision On-device Depth Perception System with Multi-Data Fusion0
SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing0
All-in-One: Transferring Vision Foundation Models into Stereo Matching0
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono FailCode3
Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed DataCode0
Superpixel Cost Volume Excitation for Stereo Matching0
Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation GraphCode2
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching0
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions0
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