Tiankun Xu
Papers
1
Total Citations
5
H-Index
1
About
Tiankun Xu has made notable contributions to the field of computer vision, with a primary focus on camera calibration techniques essential for applications in robotics and autonomous driving. His work addresses the critical challenge of achieving accurate and user-friendly camera calibration, a foundational task for reliable visual perception in these domains. Xu’s most cited paper, "Calibration Venus: An Interactive Camera Calibration Method Based on Search Algorithm and Pose Decomposition" (2020), introduces an innovative approach that enhances the stability and handleability of plane-board-based calibration methods. By integrating a search algorithm with pose decomposition, his method improves the interactive calibration process, making it more accessible and robust for practitioners. Although his citation count currently stands at 5, this work represents a practical advancement in a field where precision and ease of use are paramount. Xu’s research underscores a commitment to bridging the gap between theoretical calibration models and real-world deployment, offering tools that can streamline the setup of vision systems in dynamic environments. His contributions are particularly relevant for students and researchers seeking efficient, interactive solutions to camera calibration challenges.
Research Focus
Key Achievements
Top Papers
- 1