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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 1120 of 517 papers

TitleStatusHype
MoCha-Stereo: Motif Channel Attention Network for Stereo MatchingCode2
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical FlowCode2
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-ExpertsCode2
Iterative Geometry Encoding Volume for Stereo MatchingCode2
Event-based Stereo Depth Estimation: A SurveyCode2
GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised MatchingCode2
CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry InteractionCode2
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
BANet: Bilateral Aggregation Network for Mobile Stereo MatchingCode2
Attention Concatenation Volume for Accurate and Efficient Stereo MatchingCode2
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