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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 191–200 of 517 papers

TitleStatusHype
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
High-Resolution Depth Maps Based on TOF-Stereo Fusion—0
A Learned Stereo Depth System for Robotic Manipulation in Homes—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
A Comparative Evaluation of SGM Variants (including a New Variant, tMGM) for Dense Stereo Matching—0
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform—0
Gaussian Mixture based Evidential Learning for Stereo Matching—0
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation—0
Genetic Stereo Matching Algorithm with Fuzzy Fitness—0
Epipolar Geometry Based On Line Similarity—0
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