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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

Showing 5160 of 517 papers

TitleStatusHype
Discrete Time Convolution for Fast Event-Based StereoCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo SystemsCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching TransformerCode1
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
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