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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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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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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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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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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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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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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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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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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...
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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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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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Optimization for image stereo-matching using deep reinforcement learning in rule constraints and parallax estimation
Stereo-matching is a hot topic in the field of visual image research, to address the low image-matching accuracy of traditional algorithms. In this...
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Sliding space-disparity transformer for stereo matching
Transformers have achieved impressive performance in natural language processing and computer vision, including text translation, semantic...
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A new stereo matching energy model based on image local features
This paper constructs an energy model based on local features used in stereo matching. The local features include the similarity between different...
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SA-Net: Scene-Aware Network for Cross-domain Stereo Matching
Although the recent stereo matching methods based on deep learning achieve unprecedented state-of-the-art performance, the accuracy of these...
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Stereo-RSSF: stereo robust sparse scene-flow estimation
Scene-flow (SF) estimation is considered to be one of the most fundamental problems in scene understanding and autonomous control. The majority of...
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Deep Stereo Matching with Superpixel Based Feature and Cost
Previous stereo methods achieved state-of-the-art performances but are still difficult to handle the well-known edge-fattening issue at depth...