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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
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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