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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 2130 of 517 papers

TitleStatusHype
CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry InteractionCode2
Iterative Geometry Encoding Volume for Stereo MatchingCode2
MoCha-Stereo: Motif Channel Attention Network for Stereo MatchingCode2
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular BackbonesCode2
Neural Markov Random Field for Stereo MatchingCode2
BANet: Bilateral Aggregation Network for Mobile Stereo MatchingCode2
Attention Concatenation Volume for Accurate and Efficient Stereo MatchingCode2
Accurate and Efficient Stereo Matching via Attention Concatenation VolumeCode2
ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo MatchingCode2
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