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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 311–320 of 517 papers

TitleStatusHype
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation—0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
Fast Non-local Stereo Matching based on Hierarchical Disparity Prediction—0
Fine-tuning deep learning models for stereo matching using results from semi-global matching—0
FoggyStereo: Stereo Matching With Fog Volume Representation—0
FPGA-based Binocular Image Feature Extraction and Matching System—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
Generalizable Novel-View Synthesis using a Stereo Camera—0
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation—0
Genetic Stereo Matching Algorithm with Fuzzy Fitness—0
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