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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
FoundationStereo: Zero-Shot Stereo MatchingCode7
IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo MatchingCode4
MonSter: Marry Monodepth to Stereo Unleashes PowerCode4
A Survey on Deep Stereo Matching in the TwentiesCode4
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono FailCode3
DEFOM-Stereo: Depth Foundation Model Based Stereo MatchingCode3
An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022Code3
GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo ViewsCode3
Unifying Flow, Stereo and Depth EstimationCode3
RoadBEV: Road Surface Reconstruction in Bird's Eye ViewCode3
MoCha-Stereo: Motif Channel Attention Network for Stereo MatchingCode2
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object DetectionCode2
GeoMVSNet: Learning Multi-View Stereo With Geometry PerceptionCode2
Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation GraphCode2
QuadTree Attention for Vision TransformersCode2
Event-based Stereo Depth Estimation: A SurveyCode2
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo MatchingCode2
Robust Synthetic-to-Real Transfer for Stereo MatchingCode2
ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo MatchingCode2
Robust Confidence Intervals in Stereo Matching using Possibility TheoryCode2
Selective-Stereo: Adaptive Frequency Information Selection for Stereo MatchingCode2
Temporally Consistent Stereo MatchingCode2
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular BackbonesCode2
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-ExpertsCode2
Neural Markov Random Field for Stereo MatchingCode2
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical FlowCode2
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
Attention Concatenation Volume for Accurate and Efficient Stereo MatchingCode2
GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised MatchingCode2
CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry InteractionCode2
Accurate and Efficient Stereo Matching via Attention Concatenation VolumeCode2
Iterative Geometry Encoding Volume for Stereo MatchingCode2
BANet: Bilateral Aggregation Network for Mobile Stereo MatchingCode2
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive CorrelationCode2
GA-Net: Guided Aggregation Net for End-to-end Stereo MatchingCode1
Adaptive confidence thresholding for monocular depth estimationCode1
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo SystemsCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
Active Perception with A Monocular Camera for Multiscopic VisionCode1
Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo MethodsCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
Epipolar TransformersCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo MatchingCode1
Global Occlusion-Aware Transformer for Robust Stereo MatchingCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
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