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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 26–50 of 517 papers

TitleStatusHype
GeoMVSNet: Learning Multi-View Stereo With Geometry PerceptionCode2
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical FlowCode2
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object DetectionCode2
Accurate and Efficient Stereo Matching via Attention Concatenation VolumeCode2
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular BackbonesCode2
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive CorrelationCode2
Attention Concatenation Volume for Accurate and Efficient Stereo MatchingCode2
QuadTree Attention for Vision TransformersCode2
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo MatchingCode2
Multi-Label Stereo Matching for Transparent Scene Depth EstimationCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
LightEndoStereo: A Real-time Lightweight Stereo Matching Method for Endoscopy ImagesCode1
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching TransformerCode1
OmniStereo: Real-time Omnidireactional Depth Estimation with Multiview Fisheye CamerasCode1
Self-Assessed Generation: Trustworthy Label Generation for Optical Flow and Stereo Matching in Real-worldCode1
Pseudo-Stereo Inputs: A Solution to the Occlusion Challenge in Self-Supervised Stereo MatchingCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
LiDAR-Event Stereo Fusion with HallucinationsCode1
Temporal Event Stereo via Joint Learning with Stereoscopic FlowCode1
Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth ConversionCode1
DCVSMNet: Double Cost Volume Stereo Matching NetworkCode1
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar ImageryCode1
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgeryCode1
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