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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
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
GA-Net: Guided Aggregation Net for End-to-end Stereo MatchingCode1
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
Group-wise Correlation Stereo NetworkCode1
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
Microscopic 3D measurement of shiny surfaces based on a multi-frequency phase-shifting scheme—0
Unsupervised monocular stereo matching—0
Hierarchical Discrete Distribution Decomposition for Match Density EstimationCode0
Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique—0
Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery—0
Reconstructing 3D Motion Trajectory of Large Swarm of Flying Objects—0
Learning Depth with Convolutional Spatial Propagation NetworkCode0
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