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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

Showing 501517 of 517 papers

TitleStatusHype
Match Stereo Videos via Bidirectional Alignment0
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation0
MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications0
MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion0
Microscopic 3D measurement of shiny surfaces based on a multi-frequency phase-shifting scheme0
Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation0
MobiFuse: A High-Precision On-device Depth Perception System with Multi-Data Fusion0
Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep0
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching0
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement0
Monocular Depth Estimation with Augmented Ordinal Depth Relationships0
Multi-Level Context Ultra-Aggregation for Stereo Matching0
Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm0
Multi-scale Alternated Attention Transformer for Generalized Stereo Matching0
Multi-scale CNN stereo and pattern removal technique for underwater active stereo system0
Multi-Scale Cost Volumes Cascade Network for Stereo Matching0
Multi-scale Cross-form Pyramid Network for Stereo Matching0
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