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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 1–50 of 517 papers

TitleStatusHype
S^2M^2: Scalable Stereo Matching Model for Reliable Depth Estimation—0
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-ExpertsCode2
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather—0
ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo MatchingCode2
StereoDiff: Stereo-Diffusion Synergy for Video Depth Estimation—0
Multi-Label Stereo Matching for Transparent Scene Depth EstimationCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
M3Depth: Wavelet-Enhanced Depth Estimation on Mars via Mutual Boosting of Dual-Modal Data—0
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World—0
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo MatchingCode0
ThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based RefinementCode0
Consistency-aware Self-Training for Iterative-based Stereo Matching—0
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
LeanStereo: A Leaner Backbone based Stereo NetworkCode0
Distilling Stereo Networks for Performant and Efficient Leaner NetworksCode0
GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised MatchingCode2
RGB-Phase Speckle: Cross-Scene Stereo 3D Reconstruction via Wrapped Pre-Normalization—0
Stereo Any Video: Temporally Consistent Stereo Matching—0
BANet: Bilateral Aggregation Network for Mobile Stereo MatchingCode2
LightEndoStereo: A Real-time Lightweight Stereo Matching Method for Endoscopy ImagesCode1
SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking—0
FoundationStereo: Zero-Shot Stereo MatchingCode7
DEFOM-Stereo: Depth Foundation Model Based Stereo MatchingCode3
StereoGen: High-quality Stereo Image Generation from a Single Image—0
MonSter: Marry Monodepth to Stereo Unleashes PowerCode4
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching TransformerCode1
Gromov-Wasserstein Problem with Cyclic Symmetry—0
OmniStereo: Real-time Omnidireactional Depth Estimation with Multiview Fisheye CamerasCode1
Active Event-based Stereo VisionCode0
Learned Binocular-Encoding Optics for RGBD Imaging Using Joint Stereo and Focus Cues—0
Uncertainty Quantification in Stereo MatchingCode0
MobiFuse: A High-Precision On-device Depth Perception System with Multi-Data Fusion—0
SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing—0
All-in-One: Transferring Vision Foundation Models into Stereo Matching—0
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono FailCode3
Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed DataCode0
Superpixel Cost Volume Excitation for Stereo Matching—0
Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation GraphCode2
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching—0
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion—0
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions—0
Segmentation-aware Prior Assisted Joint Global Information Aggregated 3D Building Reconstruction—0
Self-Assessed Generation: Trustworthy Label Generation for Optical Flow and Stereo Matching in Real-worldCode1
A Lightweight Target-Driven Network of Stereo Matching for Inland WaterwaysCode0
Analysis of different disparity estimation techniques on aerial stereo image datasets—0
The Sampling-Gaussian for stereo matching—0
Pseudo-Stereo Inputs: A Solution to the Occlusion Challenge in Self-Supervised Stereo MatchingCode1
Match Stereo Videos via Bidirectional Alignment—0
Event-based Stereo Depth Estimation: A SurveyCode2
Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras—0
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