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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

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Showing 251–275 of 517 papers

TitleStatusHype
Stereo Matching Based on Visual Sensitive InformationCode1
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry—0
A Decomposition Model for Stereo MatchingCode0
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform—0
Stereo Matching by Self-supervision of Multiscopic Vision—0
CFNet: Cascade and Fused Cost Volume for Robust Stereo MatchingCode1
SMD-Nets: Stereo Mixture Density NetworksCode1
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
Physics-based Differentiable Depth Sensor Simulation—0
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation—0
Stereo Object Matching Network—0
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D DetectionCode1
ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images—0
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching—0
ES-Net: An Efficient Stereo Matching NetworkCode1
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
An underwater binocular stereo matching algorithm based on the best search domain—0
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
Multi-Scale Cost Volumes Cascade Network for Stereo Matching—0
Semi-synthesis: A fast way to produce effective datasets for stereo matching—0
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks—0
On the confidence of stereo matching in a deep-learning era: a quantitative evaluationCode1
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo Matching—0
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