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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 131–140 of 517 papers

TitleStatusHype
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatchCode0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100Code0
Cross-Scale Cost Aggregation for Stereo MatchingCode0
Active Event-based Stereo VisionCode0
Continuous 3D Label Stereo Matching using Local Expansion MovesCode0
Continual Stereo Matching of Continuous Driving Scenes With Growing ArchitectureCode0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
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