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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 276–300 of 517 papers

TitleStatusHype
Stereo Matching by Self-supervision of Multiscopic Vision—0
Stereo Matching in Time: 100+ FPS Video Stereo Matching for Extended Reality—0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions—0
Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling—0
Stereo Matching with Cost Volume based Sparse Disparity Propagation—0
Stereo Object Matching Network—0
Stereo on a budget—0
Stereo Risk: A Continuous Modeling Approach to Stereo Matching—0
StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images—0
StereoVAE: A lightweight stereo-matching system using embedded GPUs—0
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks—0
Structural inference affects depth perception in the context of potential occlusion—0
STS: Surround-view Temporal Stereo for Multi-view 3D Detection—0
Sub-pixel matching method for low-resolution thermal stereo images—0
Superpixel Cost Volume Excitation for Stereo Matching—0
SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking—0
Survey on Semantic Stereo Matching / Semantic Depth Estimation—0
SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint—0
The Global Patch Collider—0
The Sampling-Gaussian for stereo matching—0
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion—0
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework—0
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones—0
Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videos—0
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