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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 151160 of 517 papers

TitleStatusHype
High-Frequency Stereo Matching Network0
GeoMVSNet: Learning Multi-View Stereo With Geometry PerceptionCode2
Domain Generalized Stereo Matching via Hierarchical Visual Transformation0
Deep Learning of Partial Graph Matching via Differentiable Top-K0
Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity0
Structure Aggregation for Cross-Spectral Stereo Image Guided DenoisingCode1
Learning Adaptive Dense Event Stereo From the Image Domain0
Masked Representation Learning for Domain Generalized Stereo Matching0
Image-Coupled Volume Propagation for Stereo Matching0
Real-Time High-Quality Stereo Matching System on a GPU0
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