Longhai Xiang
Papers
1
Total Citations
46
H-Index
1
About
Longhai Xiang is a researcher whose work sits at the intersection of computer vision and medical imaging, with a particular focus on advancing 3D reconstruction and tracking technologies. His most-cited paper, "A Feature Matching Method based on the Convolutional Neural Network" (2023), has garnered 46 citations and addresses a critical bottleneck in feature-based 3D reconstruction: the accuracy of feature matching. Xiang demonstrates that by leveraging convolutional neural networks, the precision of matching points across images can be significantly improved, which is essential for generating reliable 3D point clouds. This contribution is especially impactful in the medical field, where accurate 3D models are vital for surgical planning, diagnostics, and patient-specific treatments. By enhancing the foundational step of feature matching, Xiang’s work helps bridge the gap between deep learning and practical medical applications, offering a pathway to more robust and automated reconstruction systems. His research underscores the importance of marrying classical computer vision techniques with modern neural architectures to solve real-world challenges in healthcare.
Research Focus
Key Achievements
Top Papers
- 1A Feature Matching Method based on the Convolutional Neural Network46 citations · 2023