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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 301–325 of 517 papers

TitleStatusHype
Multi-Scale Cost Volumes Cascade Network for Stereo Matching—0
Semi-synthesis: A fast way to produce effective datasets for stereo matching—0
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks—0
Adaptive Deconvolution-based stereo matching Net for Local Stereo Matching—0
UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo Matching—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
Direct Depth Learning Network for Stereo Matching—0
Displacement-Invariant Cost Computation for Efficient Stereo Matching—0
ADCPNet: Adaptive Disparity Candidates Prediction Network for Efficient Real-Time Stereo Matching—0
NLCA-Net v2 for Stereo Matching in ECCV'20 Robust Vision Challenge—0
Stereo Frustums: A Siamese Pipeline for 3D Object Detection—0
EDNet: Efficient Disparity Estimation with Cost Volume Combination and Attention-based Spatial Residual—0
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving—0
A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery—0
High-precision target positioning system for unmanned vehicles based on binocular vision—0
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach—0
End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences—0
Cross-Modality 3D Object Detection—0
Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow ReconstructionCode0
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100Code0
Real-time Dense Reconstruction of Tissue Surface from Stereo Optical Video—0
Real-time Surface Deformation Recovery from Stereo Videos—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
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