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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 176–200 of 517 papers

TitleStatusHype
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks—0
Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo MethodsCode1
Pseudo-LiDAR for Visual Odometry—0
STS: Surround-view Temporal Stereo for Multi-view 3D Detection—0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular BackbonesCode2
Deep Laparoscopic Stereo Matching with TransformersCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras—0
Robust and accurate depth estimation by fusing LiDAR and Stereo—0
Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?—0
MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications—0
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation—0
WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite ImagesCode1
End-to-End 3D Hand Pose Estimation from Stereo Cameras—0
Fine-tuning deep learning models for stereo matching using results from semi-global matching—0
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image MatchingCode1
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion—0
A novel stereo matching pipeline with robustness and unfixed disparity search range—0
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo—0
GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented FeatureCode1
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive CorrelationCode2
Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency PerspectiveCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
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