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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 201–225 of 517 papers

TitleStatusHype
ChiTransformer:Towards Reliable Stereo from CuesCode1
Attention Concatenation Volume for Accurate and Efficient Stereo MatchingCode2
Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation—0
Accurate Human Body Reconstruction for Volumetric Video—0
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion—0
LiDAR-guided Stereo Matching with a Spatial Consistency Constraint—0
Lightweight Multi-Drone Detection and 3D-Localization via YOLOCode1
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm—0
Stereo Matching with Cost Volume based Sparse Disparity Propagation—0
QuadTree Attention for Vision TransformersCode2
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching NetworksCode1
Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching—0
Continual Stereo Matching of Continuous Driving Scenes With Growing ArchitectureCode0
FoggyStereo: Stereo Matching With Fog Volume Representation—0
Discrete Time Convolution for Fast Event-Based StereoCode1
Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation—0
Depth Refinement for Improved Stereo Reconstruction—0
Stereoscopic Universal Perturbations across Different Architectures and DatasetsCode1
AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
Generalized Closed-form Formulae for Feature-based Subpixel Alignment in Patch-based MatchingCode0
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks—0
PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense ReconstructionCode1
TriStereoNet: A Trinocular Framework for Multi-baseline Disparity EstimationCode1
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