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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 376–400 of 517 papers

TitleStatusHype
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
Automated 3D recovery from very high resolution multi-view satellite images—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
FPGA-based Binocular Image Feature Extraction and Matching System—0
Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method—0
LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery—0
Multi-scale Cross-form Pyramid Network for Stereo Matching—0
MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching—0
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks—0
Real-Time Dense Stereo Embedded in A UAV for Road Inspection—0
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow EstimationCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume NormalizationCode0
OpenCL-based FPGA accelerator for disparity map generation with stereoscopic event camerasCode0
EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection—0
Unsupervised Cross-spectral Stereo Matching by Learning to SynthesizeCode0
Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality—0
One-view occlusion detection for stereo matching with a fully connected CRF model—0
Local Area Transform for Cross-Modality Correspondence Matching and Deep Scene Recognition—0
Unsupervised monocular stereo matching—0
Microscopic 3D measurement of shiny surfaces based on a multi-frequency phase-shifting scheme—0
Hierarchical Discrete Distribution Decomposition for Match Density EstimationCode0
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