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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 111120 of 517 papers

TitleStatusHype
Global Occlusion-Aware Transformer for Robust Stereo MatchingCode1
Quantum Annealing for Computer Vision Minimization Problems0
Color Agnostic Cross-Spectral Disparity EstimationCode0
Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain GeneralizationCode1
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning0
OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline0
Phase Guided Light Field for Spatial-Depth High Resolution 3D Imaging0
Polarimetric PatchMatch Multi-View Stereo0
MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo MatchingCode1
HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images0
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