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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 110 of 517 papers

TitleStatusHype
FoundationStereo: Zero-Shot Stereo MatchingCode7
MonSter: Marry Monodepth to Stereo Unleashes PowerCode4
IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo MatchingCode4
A Survey on Deep Stereo Matching in the TwentiesCode4
DEFOM-Stereo: Depth Foundation Model Based Stereo MatchingCode3
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono FailCode3
RoadBEV: Road Surface Reconstruction in Bird's Eye ViewCode3
GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo ViewsCode3
Unifying Flow, Stereo and Depth EstimationCode3
An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022Code3
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