Stereo Matching
150 papers with code • 0 benchmarks • 18 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
SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint
The use of synthetically generated images as an alternative, alleviates this problem but suffers from the problem of domain generalization.
Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective
The discrete optimization process poses a challenge of information loss, which restricts the level of detail that can be expressed in the generated disparity map.
Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion
To address this issue, we propose a novel stereo-LiDAR depth estimation network with Semi-Dense hint Guidance, named SDG-Depth.
Playing to Vision Foundation Model's Strengths in Stereo Matching
We believe this new paradigm will pave the way for the next generation of stereo matching networks.
Surface Reconstruction from Gaussian Splatting via Novel Stereo Views
We propose a novel approach for surface reconstruction from Gaussian splatting models.
Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching
Towards this challenge, we develop a bidirectional alignment mechanism for adjacent frames as a fundamental operation.
Intention-driven Ego-to-Exo Video Generation
Finally, the inferred human movement and high-level action descriptions jointly guide the generation of exocentric motion and interaction content (i. e., corresponding optical flow and occlusion maps) in the backward process of the diffusion model, ultimately warping them into the corresponding exocentric video.
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation
Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching.
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.
