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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 6170 of 517 papers

TitleStatusHype
Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene CompletionCode1
UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV ScenariosCode1
Structure Aggregation for Cross-Spectral Stereo Image Guided DenoisingCode1
TemporalStereo: Efficient Spatial-Temporal Stereo Matching NetworkCode1
Context-Enhanced Stereo TransformerCode1
Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo MethodsCode1
Deep Laparoscopic Stereo Matching with TransformersCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite ImagesCode1
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image MatchingCode1
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