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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 301–325 of 517 papers

TitleStatusHype
An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100Code0
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo MatchingCode1
Real-time Dense Reconstruction of Tissue Surface from Stereo Optical Video—0
Real-time Surface Deformation Recovery from Stereo Videos—0
A Multi-spectral Dataset for Evaluating Motion Estimation SystemsCode1
MTStereo 2.0: improved accuracy of stereo depth estimation withMax-treesCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo MatchingCode1
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
Visually Imbalanced Stereo Matching—0
WaveletStereo: Learning Wavelet Coefficients of Disparity Map in Stereo Matching—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds—0
Noise-Sampling Cross Entropy Loss: Improving Disparity Regression Via Cost Volume Aware Regularizer—0
Epipolar TransformersCode1
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo MatchingCode1
Expanding Sparse Guidance for Stereo Matching—0
Deep 3D Portrait from a Single ImageCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
LSM: Learning Subspace Minimization for Low-level Vision—0
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching—0
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching—0
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo MatchingCode1
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