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
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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 with no code
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching
Geometric knowledge has been shown to be beneficial for the stereo matching task.
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation
Stereo matching under foggy scenes remains a challenging task since the scattering effect degrades the visibility and results in less distinctive features for dense correspondence matching.
Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment
In this paper, we have proposed a new strategy of using the landmark anchor node instead of a radio-based anchor node to obtain the virtual coordinates (landmarkID, DISTANCE) of moving troops or defense forces that will help in tracking and maneuvering the troops along a safe path within a GPS-denied battlefield environment.
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency
In stereo-matching knowledge distillation methods of the self-supervised monocular depth estimation, the stereo-matching network's knowledge is distilled into a monocular depth network through pseudo-depth maps.
Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep
We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learning-based approaches, especially in safety-critical systems.
S$^3$M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving
Hence, in this article, we introduce S$^3$M-Net, a novel joint learning framework developed to perform semantic segmentation and stereo matching simultaneously.
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue
New sensing technologies and more advanced processing algorithms are transforming computer-integrated surgery.
3D Scene Geometry Estimation from 360$^\circ$ Imagery: A Survey
This paper provides a comprehensive survey on pioneer and state-of-the-art 3D scene geometry estimation methodologies based on single, two, or multiple images captured under the omnidirectional optics.
Left-right Discrepancy for Adversarial Attack on Stereo Networks
Stereo matching neural networks often involve a Siamese structure to extract intermediate features from left and right images.
Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids
Our approach is hybrid (i. e., quantum-classical) and is compatible with modern D-Wave quantum annealers, i. e., it includes a quadratic unconstrained binary optimization (QUBO) objective.
