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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 211220 of 517 papers

TitleStatusHype
Dive Deeper into Rectifying Homography for Stereo Camera Online Self-Calibration0
Learning Parallax for Stereo Event-based Motion Deblurring0
Stereo Matching in Time: 100+ FPS Video Stereo Matching for Extended Reality0
StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation0
DiffuVolume: Diffusion Model for Volume based Stereo Matching0
Disjoint Pose and Shape for 3D Face Reconstruction0
Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets0
Multi-scale Alternated Attention Transformer for Generalized Stereo Matching0
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal0
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