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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 211220 of 517 papers

TitleStatusHype
QuadTree Attention for Vision TransformersCode2
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching NetworksCode1
Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching0
Continual Stereo Matching of Continuous Driving Scenes With Growing ArchitectureCode0
FoggyStereo: Stereo Matching With Fog Volume Representation0
Discrete Time Convolution for Fast Event-Based StereoCode1
Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation0
Depth Refinement for Improved Stereo Reconstruction0
Stereoscopic Universal Perturbations across Different Architectures and DatasetsCode1
AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach0
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