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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 271–280 of 517 papers

TitleStatusHype
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture—0
Depth From Semi-Calibrated Stereo and Defocus—0
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling—0
Depth Refinement for Improved Stereo Reconstruction—0
Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching—0
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras—0
DiffuVolume: Diffusion Model for Volume based Stereo Matching—0
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