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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 231–240 of 517 papers

TitleStatusHype
Cross-Modality 3D Object Detection—0
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
Graph Cut based Continuous Stereo Matching using Locally Shared Labels—0
Inter-View Depth Consistency Testing in Depth Difference Subspace—0
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds—0
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks—0
Analyzing Computer Vision Data - The Good, the Bad and the Ugly—0
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms—0
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