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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 121130 of 517 papers

TitleStatusHype
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving0
Skin the sheep not only once: Reusing Various Depth Datasets to Drive the Learning of Optical Flow0
When Epipolar Constraint Meets Non-local Operators in Multi-View StereoCode1
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
SparseSat-NeRF: Dense Depth Supervised Neural Radiance Fields for Sparse Satellite ImagesCode1
DiffuVolume: Diffusion Model for Volume based Stereo Matching0
Disjoint Pose and Shape for 3D Face Reconstruction0
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