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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 281–290 of 517 papers

TitleStatusHype
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones—0
High-Resolution Depth Maps Based on TOF-Stereo Fusion—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume—0
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields—0
Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning—0
Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgeryCode0
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry—0
A Decomposition Model for Stereo MatchingCode0
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform—0
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