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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 461–470 of 517 papers

TitleStatusHype
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning—0
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
Intention-driven Ego-to-Exo Video Generation—0
Inter-View Depth Consistency Testing in Depth Difference Subspace—0
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization—0
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks—0
Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment—0
Learned Binocular-Encoding Optics for RGBD Imaging Using Joint Stereo and Focus Cues—0
Learning Adaptive Dense Event Stereo From the Image Domain—0
Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery—0
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