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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
MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications—0
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation—0
End-to-End 3D Hand Pose Estimation from Stereo Cameras—0
Fine-tuning deep learning models for stereo matching using results from semi-global matching—0
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion—0
A novel stereo matching pipeline with robustness and unfixed disparity search range—0
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo—0
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
Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm—0
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
Stereo Matching with Cost Volume based Sparse Disparity Propagation—0
Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching—0
FoggyStereo: Stereo Matching With Fog Volume Representation—0
Continual Stereo Matching of Continuous Driving Scenes With Growing ArchitectureCode0
Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation—0
Depth Refinement for Improved Stereo Reconstruction—0
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
Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching—0
3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks—0
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