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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 251–275 of 517 papers

TitleStatusHype
CATS: A Color and Thermal Stereo Benchmark—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map—0
Confidence Inference for Focused Learning in Stereo Matching—0
Consistency-aware Self-Training for Iterative-based Stereo Matching—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction—0
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
Cross-Modality 3D Object Detection—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs—0
Deep Learning of Partial Graph Matching via Differentiable Top-K—0
Deep Material-Aware Cross-Spectral Stereo Matching—0
Deep Stereo Matching with Dense CRF Priors—0
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture—0
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo—0
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue—0
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task—0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture—0
Depth From Semi-Calibrated Stereo and Defocus—0
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
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