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Guided aggregation and disparity refinement for real-time stereo matching
Stereo matching methods based on convolution neural network (CNN) often face challenges such as edge blurring and the loss of small structures. These...
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EBStereo: edge-based loss function for real-time stereo matching
Deep learning-based stereo matching has made significant progress, but it still faces challenges: The disparity prediction error maps of current...
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Real-time stereo matching with high accuracy via Spatial Attention-Guided Upsampling
Deep learning-based stereo matching methods have made remarkable progress in recent years. However, it is still a challenging task to achieve high...
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Multilevel Disparity Reconstruction Network for Real-Time Stereo Matching
Recently, stereo matching algorithms based on end-to-end convolutional neural networks achieve excellent performance far exceeding traditional...
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Research and implementation of adaptive stereo matching algorithm based on ZYNQ
Stereo matching is an important method in computer vision for simulating human binocular vision to acquire spatial distance information. Implementing...
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Supervised biadjacency networks for stereo matching
Convolutional neural network (CNN) based stereo matching methods using cost volume techniques have gained prominence in stereo matching....
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Exploring the Usage of Pre-trained Features for Stereo Matching
For many vision tasks, utilizing pre-trained features results in improved performance and consistently benefits from the rapid advancement of...
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Real-time stereo semi-global matching for video processing using previous incremental information
This paper presents an incremental stereo algorithm designed to calculate a real-time disparity image. The algorithm is designed for stereo video...
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Dynamic programming with adaptive and self-adjusting penalty for real-time accurate stereo matching
Dense disparity map extraction is one of the most active research areas in computer vision. It tries to recover three-dimensional information from a...
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An improved binocular stereo matching algorithm based on AANet
Stereo matching is an important part of establishing stereo vision. Parallax information obtained by stereo matching directly affects the...
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GPDF-Net: geometric prior-guided stereo matching with disparity fusion refinement
Stereo matching is a popular topic in the image processing and computer vision fields. Although deep learning-based stereo matching approaches have...
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A digital speckle stereo matching algorithm based on epipolar line correction
When the digital speckle correlation method captures images under certain working conditions, the extreme tilt of the camera leads to a weak...
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Few-Shot Stereo Matching with High Domain Adaptability Based on Adaptive Recursive Network
Deep learning based stereo matching algorithms have been extensively researched in areas such as robot vision and autonomous driving due to their...
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Structured support vector machine with coarse-to-fine PatchMatch filtering for stereo matching
In the past decades, a variety of learning-based algorithms have been emerged to try to explore a better solution for stereo matching by leveraging...
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Recurrent convolutional model based on gated spiking neural P system for stereo matching networks
The rapid development of deep learning techniques has introduced extensive research improvements to various aspects in the processing pipeline of the...
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An application of stereo matching algorithm based on transfer learning on robots in multiple scenes
Robot vision technology based on binocular vision holds tremendous potential for development in various fields, including 3D scene reconstruction,...
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Multi-scale inputs and context-aware aggregation network for stereo matching
Despite the significant progress made in deep learning-based stereo matching, the accuracy of these methods significantly decreases when faced with...
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An efficient and accurate multi-level cascaded recurrent network for stereo matching
With the advent of Transformer-based convolutional neural networks, stereo matching algorithms have achieved state-of-the-art accuracy in disparity...
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Guiding Deep Learning with Expert Knowledge for Dense Stereo Matching
Dense depth information can be reconstructed from stereo images using conventional hand-crafted as well as deep learning-based approaches. While...
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Coatrsnet: Fully Exploiting Convolution and Attention for Stereo Matching by Region Separation
Stereo matching is a fundamental technique for many vision and robotics applications. State-of-the-art methods either employ convolutional neural...
