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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 8190 of 517 papers

TitleStatusHype
Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth ConversionCode1
MoCha-Stereo: Motif Channel Attention Network for Stereo MatchingCode2
RoadBEV: Road Surface Reconstruction in Bird's Eye ViewCode3
Playing to Vision Foundation Model's Strengths in Stereo Matching0
Robust Confidence Intervals in Stereo Matching using Possibility TheoryCode2
GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo ViewsCode3
Neural Markov Random Field for Stereo MatchingCode2
Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching0
Intention-driven Ego-to-Exo Video Generation0
Robust Synthetic-to-Real Transfer for Stereo MatchingCode2
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