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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 451–500 of 517 papers

TitleStatusHype
HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images—0
High-Frequency Stereo Matching Network—0
High-precision target positioning system for unmanned vehicles based on binocular vision—0
High-Resolution Depth Maps Based on TOF-Stereo Fusion—0
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry—0
HyperDepth: Learning Depth From Structured Light Without Matching—0
Hyperspectral Light Field Stereo Matching—0
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform—0
Image-Coupled Volume Propagation for Stereo Matching—0
Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique—0
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning—0
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
Intention-driven Ego-to-Exo Video Generation—0
Inter-View Depth Consistency Testing in Depth Difference Subspace—0
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization—0
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks—0
Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment—0
Learned Binocular-Encoding Optics for RGBD Imaging Using Joint Stereo and Focus Cues—0
Learning Adaptive Dense Event Stereo From the Image Domain—0
Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery—0
Learning Dense Wide Baseline Stereo Matching for People—0
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching—0
Learning Parallax for Stereo Event-based Motion Deblurring—0
Learning Patch Reconstructability for Accelerating Multi-View Stereo—0
Learning Residual Flow as Dynamic Motion from Stereo Videos—0
Learning the Matching Function—0
Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching—0
Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps—0
Left-Right Comparative Recurrent Model for Stereo Matching—0
Left-right Discrepancy for Adversarial Attack on Stereo Networks—0
Leveraging Stereo Matching With Learning-Based Confidence Measures—0
LiDAR-guided Stereo Matching with a Spatial Consistency Constraint—0
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields—0
Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras—0
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion—0
LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation—0
LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery—0
Local Area Transform for Cross-Modality Correspondence Matching and Deep Scene Recognition—0
Local Convolutional Features With Unsupervised Training for Image Retrieval—0
Local Detection of Stereo Occlusion Boundaries—0
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks—0
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach—0
Look Wider to Match Image Patches with Convolutional Neural Networks—0
LoS: Local Structure-Guided Stereo Matching—0
LSM: Learning Subspace Minimization for Low-level Vision—0
M3Depth: Wavelet-Enhanced Depth Estimation on Mars via Mutual Boosting of Dual-Modal Data—0
MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling—0
MAP Disparity Estimation Using Hidden Markov Trees—0
Masked Representation Learning for Domain Generalized Stereo Matching—0
Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching—0
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