Tang Fan
Papers
1
Total Citations
9
H-Index
1
About
Tang Fan’s research bridges computational geometry and signal processing, with a focus on robust methods for handling noisy, real-world sensor data. His most-cited work, “Convergent Smoothing and Segmentation of Noisy Range Data in Multiscale Space” (2008, 9 citations), addresses a critical gap: most noise reduction and segmentation algorithms were designed for images, not range data. Fan introduced an adaptive smoothing algorithm that operates within a scale-space framework, using model-based masks to simultaneously denoise and segment 3D range scans. This convergent approach ensures stability across scales, making it valuable for applications in robotics, autonomous navigation, and 3D reconstruction. While his citation count reflects a specialized niche, the work’s practical impact lies in its principled handling of depth sensor noise—a foundational challenge in computer vision. Fan’s contribution stands as a methodical step toward more reliable perception systems, demonstrating how careful mathematical modeling can solve real-world data degradation problems.
Research Focus
Key Achievements
Top Papers
- 1