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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 251–300 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
DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems—0
A Learned Stereo Depth System for Robotic Manipulation in Homes—0
Survey on Semantic Stereo Matching / Semantic Depth Estimation—0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion—0
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
Stereo Matching by Self-supervision of Multiscopic Vision—0
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
Physics-based Differentiable Depth Sensor Simulation—0
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation—0
Stereo Object Matching Network—0
ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images—0
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching—0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
An underwater binocular stereo matching algorithm based on the best search domain—0
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