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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 2130 of 517 papers

TitleStatusHype
SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking0
FoundationStereo: Zero-Shot Stereo MatchingCode7
DEFOM-Stereo: Depth Foundation Model Based Stereo MatchingCode3
MonSter: Marry Monodepth to Stereo Unleashes PowerCode4
StereoGen: High-quality Stereo Image Generation from a Single Image0
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching TransformerCode1
Learned Binocular-Encoding Optics for RGBD Imaging Using Joint Stereo and Focus Cues0
Gromov-Wasserstein Problem with Cyclic Symmetry0
Active Event-based Stereo VisionCode0
OmniStereo: Real-time Omnidireactional Depth Estimation with Multiview Fisheye CamerasCode1
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