Stereo Matching
150 papers with code • 0 benchmarks • 19 datasets
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
Benchmarks
These leaderboards are used to track progress in Stereo Matching
Libraries
Use these libraries to find Stereo Matching models and implementationsDatasets
Virtual KITTI
ETH3D
Middlebury 2014
DrivingStereo
Virtual KITTI 2
PST900
IMC PhotoTourism
3D Ken Burns
Middlebury 2005
CATS
Latest papers
RomniStereo: Recurrent Omnidirectional Stereo Matching
To bridge the gap between OSM and RAFT, we mainly propose an opposite adaptive weighting scheme to seamlessly transform the outputs of spherical sweeping of OSM into the required inputs for the recurrent update, thus creating a recurrent omnidirectional stereo matching (RomniStereo) algorithm.
S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar Imagery
cveo/s3net
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Stereo matching and semantic segmentation are significant tasks in binocular satellite 3D reconstruction.
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery
Conclusion: The proposed BDIS-SLAM is a lightweight stereo dense SLAM system for MIS.
Global Occlusion-Aware Transformer for Robust Stereo Matching
Despite the remarkable progress facilitated by learning-based stereo-matching algorithms, the performance in the ill-conditioned regions, such as the occluded regions, remains a bottleneck.
Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization
bartn8/vppdc
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This paper proposes a new framework for depth completion robust against domain-shifting issues.
OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline
Based on OpenStereo, we conducted experiments and have achieved or surpassed the performance metrics reported in the original paper.
MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching
This architecture mitigates the multi-peak distribution problem in matching through the multi-peak lookup strategy, and integrates the coarse-to-fine concept into the iterative framework via the cascade search range.
When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo
tqtqliu/et-mvsnet
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ICCV 2023
This constraint reduces the 2D search space into the epipolar line in stereo matching.
SparseSat-NeRF: Dense Depth Supervised Neural Radiance Fields for Sparse Satellite Images
However, NeRF and its variants require many views to produce convincing scene's geometries which in earth observation satellite imaging is rare.
ELFNet: Evidential Local-global Fusion for Stereo Matching
jimmy19991222/elfnet
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ICCV 2023
Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation.
