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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 326–350 of 517 papers

TitleStatusHype
Gromov-Wasserstein Problem with Cyclic Symmetry—0
HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images—0
Appearance and Shape from Water Reflection—0
Shift Convolution Network for Stereo Matching—0
Simultaneous Video Defogging and Stereo Reconstruction—0
Skin the sheep not only once: Reusing Various Depth Datasets to Drive the Learning of Optical Flow—0
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization—0
Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation—0
Spatial, Temporal, and Geometric Fusion for Remote Sensing Images—0
Stereo 3D Object Trajectory Reconstruction—0
Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed Data—0
Stereo Any Video: Temporally Consistent Stereo Matching—0
Stereo Computation for a Single Mixture Image—0
StereoDiff: Stereo-Diffusion Synergy for Video Depth Estimation—0
Stereo Event Lifetime and Disparity Estimation for Dynamic Vision Sensors—0
StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation—0
Stereo Frustums: A Siamese Pipeline for 3D Object Detection—0
StereoGen: High-quality Stereo Image Generation from a Single Image—0
Stereo Matching by Joint Energy Minimization—0
Stereo Matching by Self-supervision of Multiscopic Vision—0
Stereo Matching in Time: 100+ FPS Video Stereo Matching for Extended Reality—0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions—0
Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling—0
Stereo Matching with Cost Volume based Sparse Disparity Propagation—0
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