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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

Showing 481490 of 517 papers

TitleStatusHype
Improved Stereo Matching with Constant Highway Networks and Reflective Confidence LearningCode0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling AutonomyCode0
A hybrid algorithm for disparity calculation from sparse disparity estimates based on stereo visionCode0
Unsupervised Stereo Matching Network For VHR Remote Sensing Images Based On Error PredictionCode0
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded DevicesCode0
A Decomposition Model for Stereo MatchingCode0
ASV: Accelerated Stereo Vision SystemCode0
An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100Code0
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth PredictionCode0
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