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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 261270 of 517 papers

TitleStatusHype
Simultaneous Video Defogging and Stereo Reconstruction0
Skin the sheep not only once: Reusing Various Depth Datasets to Drive the Learning of Optical Flow0
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization0
Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation0
Spatial, Temporal, and Geometric Fusion for Remote Sensing Images0
Stereo 3D Object Trajectory Reconstruction0
Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed Data0
Stereo Any Video: Temporally Consistent Stereo Matching0
Stereo Computation for a Single Mixture Image0
StereoDiff: Stereo-Diffusion Synergy for Video Depth Estimation0
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