SOTAVerified

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 161170 of 517 papers

TitleStatusHype
Real-Time High-Quality Stereo Matching System on a GPU0
TemporalStereo: Efficient Spatial-Temporal Stereo Matching NetworkCode1
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical FlowCode2
Unifying Flow, Stereo and Depth EstimationCode3
Self-Supervised Intensity-Event Stereo Matching0
Expansion of Visual Hints for Improved Generalization in Stereo Matching0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map0
2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation0
A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images0
An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022Code3
Show:102550
← PrevPage 17 of 52Next →

No leaderboard results yet.