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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 401–410 of 517 papers

TitleStatusHype
Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique—0
Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery—0
Reconstructing 3D Motion Trajectory of Large Swarm of Flying Objects—0
Learning Depth with Convolutional Spatial Propagation NetworkCode0
Semi-dense Stereo Matching using Dual CNNs—0
DSR: Direct Self-rectification for Uncalibrated Dual-lens CamerasCode0
Confidence Inference for Focused Learning in Stereo Matching—0
Real-Time Stereo Vision on FPGAs with SceneScan—0
DSVO: Direct Stereo Visual Odometry—0
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement—0
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