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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 201–250 of 517 papers

TitleStatusHype
ChiTransformer:Towards Reliable Stereo from CuesCode1
Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation—0
Attention Concatenation Volume for Accurate and Efficient Stereo MatchingCode2
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
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
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
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks—0
Generalized Closed-form Formulae for Feature-based Subpixel Alignment in Patch-based MatchingCode0
PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense ReconstructionCode1
TriStereoNet: A Trinocular Framework for Multi-baseline Disparity EstimationCode1
Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching—0
3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks—0
Stereo Hybrid Event-Frame (SHEF) Cameras for 3D PerceptionCode1
DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems—0
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo MatchingCode1
A Learned Stereo Depth System for Robotic Manipulation in Homes—0
Survey on Semantic Stereo Matching / Semantic Depth Estimation—0
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo MatchingCode2
Learning Signed Distance Field for Multi-view Surface ReconstructionCode1
MobileStereoNet: Towards Lightweight Deep Networks for Stereo MatchingCode1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume ExcitationCode1
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
Neural Rays for Occlusion-aware Image-based RenderingCode1
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
Bilateral Grid Learning for Stereo Matching NetworksCode1
Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning—0
ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite ImagesCode1
Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgeryCode0
SRH-Net: Stacked Recurrent Hourglass Network for Stereo MatchingCode1
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