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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 381–390 of 517 papers

TitleStatusHype
Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method—0
LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery—0
Multi-scale Cross-form Pyramid Network for Stereo Matching—0
MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching—0
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks—0
Real-Time Dense Stereo Embedded in A UAV for Road Inspection—0
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow EstimationCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
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