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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 151–200 of 517 papers

TitleStatusHype
Real-Time Variational Fisheye Stereo without Rectification and UndistortionCode0
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale OptimizationCode0
A Lightweight Target-Driven Network of Stereo Matching for Inland WaterwaysCode0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow EstimationCode0
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo MatchingCode0
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded DevicesCode0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
A hybrid algorithm for disparity calculation from sparse disparity estimates based on stereo visionCode0
OpenCL-based FPGA accelerator for disparity map generation with stereoscopic event camerasCode0
A Flexible Recursive Network for Video Stereo Matching Based on Residual EstimationCode0
CBMV: A Coalesced Bidirectional Matching Volume for Disparity EstimationCode0
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
Efficient stereo matching on embedded GPUs with zero-means cross correlationCode0
Efficient Deep Learning for Stereo MatchingCode0
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo MatchingCode0
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
RomniStereo: Recurrent Omnidirectional Stereo MatchingCode0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
DSR: Direct Self-rectification for Uncalibrated Dual-lens CamerasCode0
LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost AggregationCode0
A Decomposition Model for Stereo MatchingCode0
OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong BaselineCode0
Domain-invariant Stereo Matching NetworksCode0
Binary Stereo MatchingCode0
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume NormalizationCode0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Distilling Stereo Networks for Performant and Efficient Leaner NetworksCode0
Color Agnostic Cross-Spectral Disparity EstimationCode0
Learning for Disparity Estimation through Feature ConstancyCode0
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost VolumeCode0
Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgeryCode0
Computing the Stereo Matching Cost with a Convolutional Neural NetworkCode0
Fast Feature Extraction with CNNs with Pooling LayersCode0
Improved Stereo Matching with Constant Highway Networks and Reflective Confidence LearningCode0
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling AutonomyCode0
Digging Into Normal Incorporated Stereo MatchingCode0
LeanStereo: A Leaner Backbone based Stereo NetworkCode0
Guided Stereo MatchingCode0
Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise LabelingCode0
Adaptive Unimodal Cost Volume Filtering for Deep Stereo MatchingCode0
Hierarchical Deep Stereo Matching on High-resolution ImagesCode0
ASV: Accelerated Stereo Vision SystemCode0
Hierarchical Discrete Distribution Decomposition for Match Density EstimationCode0
Learning Depth with Convolutional Spatial Propagation NetworkCode0
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution—0
Depth From Semi-Calibrated Stereo and Defocus—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
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