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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 351–400 of 517 papers

TitleStatusHype
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatchCode0
Adaptive Unimodal Cost Volume Filtering for Deep Stereo MatchingCode0
Robust Full-FoV Depth Estimation in Tele-wide Camera System—0
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task—0
Multi-Spectral Visual Odometry without Explicit Stereo Matching—0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
Stereo Event Lifetime and Disparity Estimation for Dynamic Vision Sensors—0
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching—0
Appearance and Shape from Water Reflection—0
TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching—0
Local Detection of Stereo Occlusion Boundaries—0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios—0
Multi-Level Context Ultra-Aggregation for Stereo Matching—0
DISCO: Depth Inference from Stereo using Context—0
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale OptimizationCode0
Guided Stereo MatchingCode0
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images—0
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model—0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
Automated 3D recovery from very high resolution multi-view satellite images—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—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
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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