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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 76–100 of 517 papers

TitleStatusHype
Spatial, Temporal, and Geometric Fusion for Remote Sensing Images—0
Generalizable Novel-View Synthesis using a Stereo Camera—0
Advancing Applications of Satellite Photogrammetry: Novel Approaches for Built-up Area Modeling and Natural Environment Monitoring using Stereo/Multi-view Satellite Image-derived 3D Data—0
SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint—0
Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective—0
Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth ConversionCode1
MoCha-Stereo: Motif Channel Attention Network for Stereo MatchingCode2
RoadBEV: Road Surface Reconstruction in Bird's Eye ViewCode3
Robust Confidence Intervals in Stereo Matching using Possibility TheoryCode2
Playing to Vision Foundation Model's Strengths in Stereo Matching—0
GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo ViewsCode3
Neural Markov Random Field for Stereo MatchingCode2
Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching—0
Intention-driven Ego-to-Exo Video Generation—0
Robust Synthetic-to-Real Transfer for Stereo MatchingCode2
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation—0
Selective-Stereo: Adaptive Frequency Information Selection for Stereo MatchingCode2
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
Digging Into Normal Incorporated Stereo MatchingCode0
DCVSMNet: Double Cost Volume Stereo Matching NetworkCode1
Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment—0
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling AutonomyCode0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
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