Optimizations and Applications of Binocular Vision Technology Development by Deep Learning Article Swipe
Traditional binocular vision technology exhibits poor robustness in weak texture, illumination change, and occluded scenes, along with high computational complexity, making it difficult to meet real-time requirements. The rapid development of deep learning has brought revolutionary breakthroughs to this field. This paper systematically reviews the optimization methods and application progress of deep learning-driven binocular vision technology. Its purpose is to analyze the core foundations of binocular vision and deep learning, clarify the key optimization directions of the technology, and provide references for its practical application and future development. The research starts with the core technical foundations of binocular vision and deep learning, then explores two key dimensions: stereo matching accuracy optimization and inference efficiency optimization. It also summarizes the application of the technology in fields such as industrial manufacturing, autonomous driving, and robotics. The results show that deep learning has realized a paradigm shift in binocular vision from passively following manual rules to actively learning scene geometry, significantly improving its accuracy, robustness, and efficiency. The paper also points out shortcomings such as insufficient discussion on model generalization and quantitative comparison of computational complexity, and prospects for future research directions like self-supervised learning and multi-sensor fusion.
Related Topics
Concepts
Deep learning
Artificial intelligence
Computer science
Robustness (evolution)
Inference
Stereopsis
Binocular vision
Computer vision
Generalization
Key (lock)
Binocular disparity
Machine vision
Matching (statistics)
Deep neural networks
Core (optical fiber)
Stereo cameras
Technology development
Image processing
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.54254/2755-2721/2025.ld29547
- OA Status
- hybrid
- OpenAlex ID
- https://openalex.org/W7104703394
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W7104703394Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.54254/2755-2721/2025.ld29547Digital Object Identifier
- Title
-
Optimizations and Applications of Binocular Vision Technology Development by Deep LearningWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-11Full publication date if available
- Authors
-
Xinyu WenList of authors in order
- Landing page
-
https://doi.org/10.54254/2755-2721/2025.ld29547Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.54254/2755-2721/2025.ld29547Direct OA link when available
- Concepts
-
Deep learning, Artificial intelligence, Computer science, Robustness (evolution), Inference, Stereopsis, Binocular vision, Computer vision, Generalization, Key (lock), Binocular disparity, Machine vision, Matching (statistics), Deep neural networks, Core (optical fiber), Stereo cameras, Technology development, Image processingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
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